Other / unclassifiable — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 11 September 2026 and assigned it the closest of 31 fixed categories, at 71% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
29 measurements spanning 41 days, net -36. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 10,907–10,994 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 29
Measured (UTC)
Subscribers
Change
17 Sept 2026, 13:19
10,948
+22
15 Sept 2026, 09:20
10,926
+6
13 Sept 2026, 21:38
10,920
-4
12 Sept 2026, 01:19
10,924
+7
9 Sept 2026, 17:55
10,917
-9
6 Sept 2026, 12:15
10,926
+3
4 Sept 2026, 03:38
10,923
-13
2 Sept 2026, 20:46
10,936
-3
1 Sept 2026, 17:12
10,939
-3
31 Aug 2026, 19:05
10,942
+5
30 Aug 2026, 20:28
10,937
-8
28 Aug 2026, 17:44
10,945
-1
27 Aug 2026, 15:34
10,946
-5
26 Aug 2026, 17:37
10,951
-2
25 Aug 2026, 16:08
10,953
-3
24 Aug 2026, 17:06
10,956
-1
23 Aug 2026, 00:37
10,957
-4
21 Aug 2026, 10:33
10,961
-11
20 Aug 2026, 12:42
10,972
+8
19 Aug 2026, 10:22
10,964
first reading
Engagement
33 posts held, back to 12 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 42 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
8.75%
avg views ÷ 10,948 subscribers
Avg views / post
958
8 posts measured
Reaction rate
3.83%
reactions ÷ views · ER floor
Posts in window
8
of 33 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 1 September 2026
Posts held
33 (12 July 2026 – 1 September 2026)
Views total
7,660
Reactions total
293
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 02:32 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
2m 53s
Average length
1m 27s
Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
1,855 reactions across 33 posts, in 31 distinct kinds. The most used accounts for 26.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
487
26.3%
👍
235
12.7%
👏
216
11.6%
🔥
127
6.85%
🏆
89
4.80%
custom 5271762396640200492
79
4.26%
🎉
62
3.34%
✍
58
3.13%
💯
58
3.13%
🕊
52
2.80%
🗿
51
2.75%
custom 5188570838621775301
46
2.48%
🐳
42
2.26%
🙏
37
1.99%
🤬
32
1.73%
custom 4985738632951235046
30
1.62%
😢
30
1.62%
😍
21
1.13%
custom 5395751191873331619
19
1.02%
😱
12
0.647%
11 further kinds
72
3.88%
Custom emoji. 4 of the rows above are Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The counts beside them are Telegram’s.
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 33 of the 33 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 1,855 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 33 most recent posts we hold, published 12 July 2026 to 1 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram Stars
Stars received
60
across the posts below
Posts paid on
32
of 33 we hold a reading for · 97%
Most on one post
14
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @dissertaciya_malinovich. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 33 most recent posts we hold for this entry, published 12 July 2026 to 1 September 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Представляем вам спикерок первого дня конференции! ❤️🩹
Конференция «Женское движение в России — 2026» пройдет в ближайшие выходные — 5 и 6 сентября.
❤️🩹Не забудьте зарегистрироваться, чтобы послушать выступления в прямом эфире: https://forms.gle/v9G61XC9GDdRXyUy5
Скоро мы познакомим вас со спикерками второго дня!
@alt_fem
Многие из нас, как и я, выступают на конференциях. Организаторкам часто нужна свежая портретная фотография для афиши или брошюры.
Так я подготовила актуальное фото для конференции «Женское движение в России 2026»: заменила фон, цвет волос и сделала более строгую укладку. Выступаю 5 сентября в 14:25 (подробности напишу позже).
Сохраняйте промпт:
«Отредактируй загруженную фотографию и создай профессиональный портрет …
AI-челлендж продолжается на очной встрече сегодня
Вчера мы перешли от подготовительной работы к структуре научного текста. Сегодня предлагаю проверить, действительно ли этот каркас можно превращать в статью.
ПРОМПТ ДНЯ:
«Выступи в роли научной редакторки. Я пришлю тебе структуру моей научной статьи.
Проверь:
• все ли разделы необходимы для раскрытия темы;
• соответствует ли структура цели и задачам исследования;
• …
План до конца августа
У меня сразу 4 пункта нашего плана на последние дни августа ⬇️
1. AI-ЧЕЛЛЕНДЖ продолжается.
Вчера мы устроили аудит того, что сделали за первые недели. Следующее задание будет уже про переход от подготовительной работы непосредственно к структуре научного текста.
Я бы дала не промпт «напиши статью», а промпт на создание подробного каркаса. Это логично после вчерашнего аудита и не позволяет ней…
Здравствуйте, я присоединалась к челленджу 4 дня назад , что я смогла сделать:
1. я просмотрела образовательные видео на ютуб канале
2. поняла для себя и сформулировала задачи моей магистерской так, чтобы в теоретической и практической части были ответы или решения этих задач.
3. соответственно я сформулировала темы параграфов для теоретической и практической глав моей магистерской.
4. Я сформулировала цель моей раб…
AI-челлендж продолжается. Но теперь без ежедневной гонки
Первые 5 дней мы выбирали темы статей, искали литературу, работали с аннотациями и приводили всё это в систему.
А сегодня предлагаю сделать очень полезную вещь: не писать дальше, пока нейросеть не проверит то, что уже сделано.
Промпт дня:
«Выступи в роли научной редакторки. Я пришлю тебе тему моей научной статьи, цель, аннотацию и список литературы.
Проведи…
Готовый промпт, который помог учиться в НИУ ВШЭ на английской программе
На английскую программу социологического факультета Вышки «Население и развитие» я поступила случайно, еще и на бюджет. Эту история рассказывала ➡️ здесь
Осознав, что я поступала на АНГЛИЙСКУЮ ПРОГРАММУ, я с ужасом поняла, что мне придётся:
• слушать лекции и быстро конспектировать;
• читать статьи на английском;
• участвовать в обсуждениях;
• …
У Вас Евгения четко понятно в работе, что за чем следует.Это ценный навык не только делиться практикой как применяется инструмент. Но ещё донести это до других так чтобы каждый понял и смог применить с пользой для себя
Добрый вечер. Решила рассказать, что получилось за пять дней, и поучаствовать в конкурсе. Я сейчас параллельно прохожу летнюю школу СНО при МГУ, поэтому часть заданий сначала показалась знакомой. Там тоже дают промты для формулирования темы, поиска источников, работы со структурой исследования. Думала, примерно представляю, что будет дальше.
Но у Евгении мне сразу понравилась другая вещь. Она не просто дает промт и п…
Я возвращаюсь к нашему AI-челленджу
Летом активность оказалась ниже, чем я ожидала, поэтому после первых пяти заданий я взяла паузу. Сейчас предлагаю спокойно продолжить и использовать уже опубликованные материалы как точку старта.
Покажите в комментариях, что удалось сделать за эти пять дней: тему, план, структуру или любой другой результат. Всем, кто поделится результатом, я отправлю полный файл AI-челленджа на 1…
Конференция «Женское движение в России — 2026»
Онлайн-конференция от команды AltLeft объединяет организаторок и участниц женского движения, ведущих свою деятельность в России.
❤️🩹Мы приглашаем к участию всех, кто работает с женщинами и участвует в развитии феминистского движения: организует мероприятия, ведет просветительские проекты, развивает женские сообщества, занимается исследованиями, оказывает юридическую, …
Showing the 12 most recent of 33 posts we hold for @dissertaciya_malinovich. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Posts edited after publishing
@dissertaciya_malinovich edited 4 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
10 August 2026
Most recent edit
27 August 2026
Forward network
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Научные конференции РИНЦ @konferen · 32,079 Telegram ranks this channel #53 of 94 here — alongside 93 others — read 14 September 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 17 September 2026 — this
entry's latest reading, not the date you are reading this.
“Малинович | Научница” (@dissertaciya_malinovich), 10,948 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/dissertaciya_malinovich.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.