National news — 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 10 September 2026 and assigned it the closest of 31 fixed categories, at 95% 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 the other topics.
Growth
56 measurements spanning 96 days, net +4,339. 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 12,033–17,702 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 56
Measured (UTC)
Subscribers
Change
10 Nov 2026, 10:11
17,027
-4
8 Nov 2026, 22:15
17,031
-4
7 Nov 2026, 16:54
17,035
-1
6 Nov 2026, 14:27
17,036
-2
5 Nov 2026, 13:07
17,038
-3
4 Nov 2026, 10:31
17,041
-3
3 Nov 2026, 11:07
17,044
-2
2 Nov 2026, 14:28
17,046
-2
1 Nov 2026, 12:25
17,048
+6
31 Oct 2026, 10:18
17,042
+22
30 Oct 2026, 08:44
17,020
+25
29 Oct 2026, 06:20
16,995
+27
28 Oct 2026, 00:55
16,968
+7
27 Oct 2026, 01:38
16,961
+1
25 Oct 2026, 06:07
16,960
-2
22 Oct 2026, 10:30
16,962
-1
21 Oct 2026, 01:07
16,963
-1
19 Oct 2026, 20:10
16,964
+24
18 Oct 2026, 22:34
16,940
+36
17 Oct 2026, 22:39
16,904
first reading
Engagement
35 posts held, back to 21 March 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 67 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
7.56%
avg views ÷ 17,027 subscribers
Avg views / post
1,290
8 posts measured
Reaction rate
6.93%
reactions ÷ views · ER floor
Posts in window
8
of 35 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 10 November 2026
Posts held
35 (21 March 2026 – 10 November 2026)
Views total
10,301
Reactions total
714
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
11 Nov 2026, 04:52 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
Photos
50
Videos
39
Links
53
Lifetime counters from Telegram’s own channel header, read 11 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Video runtime
52m 20s
Average length
3m 44s
Measured directly from 14 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
27,795 reactions across 35 posts, in 13 distinct kinds. The most used accounts for 36.8% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
10,218
36.8%
👍
10,001
36.0%
💯
2,682
9.65%
👏
1,873
6.74%
🙏
1,643
5.91%
❤
317
1.14%
😢
271
0.975%
🫡
262
0.943%
🤬
239
0.86%
🤷♂
113
0.407%
👀
94
0.338%
😁
59
0.212%
👎
23
0.083%
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 35 of the 35 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 27,795 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 35 most recent posts we hold, published 21 March 2026 to 10 November 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.
Recent posts
Telegram’s own ‘edited’ marker: 1 of 8 posts held from the last 30 days (12.5%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
⚡️25 ноября состоится V Национальный Форум «Здоровье и безопасность детей»
▪️Центральная тема Форума – «противодействие вовлечению детей и подростков в экстремистскую и террористическую деятельность».
Трансляции можно будет посмотреть в прямом эфире.
🗓 Регистрация по ссылке.
Подписывайтесь на Национальный центр помощи в MAX / ВК / ОК.
😀 Национальный Центр
Пожалуй, лучший подарок на день рождения — закончить большую коллективную работу.
Готово первое издание методического пособия «Теория маркеров. Как распознать вербовку до первого поручения».
За этим изданием стоит большой труд Национального центра помощи и всех, кто помогал нам в этой работе. Сколько было обсуждений, уточнений, правок, возвращений к уже, казалось бы, готовым страницам. Очень приятно наконец увидеть…
Мы сняли продолжение фильма "Предательство".
Ровно через неделю он выйдет в эфир на канале Россия 1.
Это, как и в прошлый раз, рассказ о тех, кто по разным причинам решил сотрудничать с врагом. Разные люди: от детей, которых просто цинично обманули до идейных предателей, которые сами вышли на вербовщиков.
Мы, когда начинали работать над вторым фильмом, честно думали, что наверное нам будет сложно рассказать что-то…
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Программа организована Российским обществом «Знание» в рамках Всероссийского форума «Большие смыслы». Участники обсуждают вопросы в…
⚡️Эксклюзив из Курской области: Первый канал и проект Wargonzo о работе отделения Национального центра помощи «Мангуст» в приграничье
Все подробности — в видео.
Подписывайтесь на Национальный центр помощи в MAX / ВК / ОК.
😀 Национальный Центр
В Брянской области проходят занятия по профилактике вовлечения подростков в противоправную деятельность
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Сегодня ещё одна встреча прошла в Клинцах на базе Кл…
«Мы не должны постоянно работать вторым номером, реактивно отвечая на уже совершенные преступления и выявленные схемы вербовки. Пока мы изучаем одну схему, противник уже применяет новые. Поэтому важно научить подростков распознавать не конкретные сценарии, а сами механизмы вовлечения. Именно в этом заключается наша идея — перейти от реактивной профилактики к превентивной, работать на опережение»
🙃 «Сервалистика» в М…
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В этом месяце заместитель директора Национального центра помощи Сергей Цапенко провёл серию профилактических лекций в школах Москвы.
С учениками 7–11-х классов говорил о том, как подростков могут пытаться вовлечь в диверсионную и другую противоправную деятельность, какие способы для этого используют и почему важно вовремя распознать угрозу.
Профила…
26 лет лишения свободы...
Истории молодых людей, для которых поиск быстрого заработка закончился приговором. Десятилетия в заключении вместо жизни, которую они могли построить. Последствия должны знать ещё до согласия на преступное задание: длительный срок заключения, отсутствие сроков давности за подобные преступления, максимально жесткие условия УДО.
Таких материалов должно быть гораздо больше. Недостаточно объясни…
🤟 Чем раньше подросток понимает механику вовлечения, тем меньше шансов, что чужой сценарий станет его реальной жизнью.
В Белгороде говорили без эвфемизмов, как есть: как подростки становятся курьерами-арестантами, почему «просто пошутить» или выполнить задание куратора из чата заканчивается реальным сроком за терроризм, и как работает анатомия шантажа — от фейковых знакомств до реальных поджогов объектов инфраструкт…
Сегодня мы вспоминаем Беслан: детей, родителей и учителей, погибших в школе, и тех, кто ценой собственной жизни спасал заложников.
Мы помним каждого.
😢163🙏40👏6🫡4❤2
Showing the 12 most recent of 35 posts we hold for @servalistika. 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.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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
Named by 7 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
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.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@servalistika named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@rian_ru named in 8 posts, 8 August 2026 – 18 August 2026
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 10 November 2026 — this
entry's latest reading, not the date you are reading this.
“Сервалистика” (@servalistika), 17,027 subscribers as measured 10 November 2026. Telegram Register, tgregister.com/channel/servalistika.
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.