Channel
@sogie_webinar
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
1,339subscribers
-4 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001497637375 |
|---|---|
| Type | Channel |
| Username | @sogie_webinar |
| Created | Between 1 June 2019 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/sogie_webinar |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 08:34 | 1,339 | -1 |
| 10 Aug 2026, 05:11 | 1,340 | -3 |
| 6 Aug 2026, 21:21 | 1,343 | no change |
| 6 Aug 2026, 14:49 | 1,343 | first reading |
17 posts held, back to 28 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 1 pageof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 27 July 2026 |
|---|---|
| Posts held | 17 (28 March 2026 – 27 July 2026) |
| Views total | 440 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 6 Aug 2026, 14:49 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.
13 reactions across 9 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 13 | 100.0% |
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 9 of the 17 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 13reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 17 most recent posts we hold, published 28 March 2026 to 27 July 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.
揭秘中国镇压机器的运作机制——以及它如何扼杀世界各地的异议 Scilla Alecci and ICIJ https://www.icij.org/investigations/china-targets/china-transnational-repression-dissent-around-world/ 国际调查记者同盟（ICIJ）发布了一项历时数月的跨国调查，采访了23个国家的105名受访者，并结合中国内部文件，揭露了中共如何在海外持续打压异议人士。 调查发现，不少生活在海外的中国人、香港人、维吾尔人和藏人，在公开发声后都会遇到类似的情况：有人被黑客攻击，有人被长期跟踪监视，有人的家属被警方约谈，还有人收到死亡威胁，甚至遭到人身攻击。 很多受访者表示，他们最大的压力不是来自自己，而是来自国内的家人。每当他们参加抗议、接受采访或公开发言后，警方很快就会找到他们的父母、亲属甚至老师，要求他们劝阻，借此迫使他们停止发声。…
从中国出发开展酷儿研究 United Proud Women 主讲人：Petrus Liu 教授 波士顿大学中文与比较文学、妇女/性别/性研究教授 时间： 太平洋时间 7月18日（周六）上午9:00 东部时间 7月18日（周六）中午12:00 北京时间 7月19日（周日）凌晨12:00 Zoom会议链接：https://zoom.us/j/91224971768 从中国视角开展酷儿研究，而非仅仅将中国作为文化差异的对象进行研究，其意义何在？在本次研讨会上，刘教授将以中国为知识视角，重新审视酷儿研究的既有假设、方法和既有范畴。 刘教授将英语世界酷儿理论的反基础主义倾向与亚洲酷儿研究中普遍存在的以经验为导向的倾向进行对比，提出酷儿性是一种知识谦逊的实践：它意味着愿意搁置既有的知识范畴，承认其历史和地缘政治的局限性，并对这些范畴可能排除的思维方式保持开放的态度。 刘彼得是波士顿大学中国与比较文学教授，同时也是女性、性别与性…
https://www.hklgbtqarchive.com/zh
❤1
「早期（1998年）台灣同志諮詢熱線協會剛成立的時候，大家打電話來，可能連『同性戀』或『同志』都說不出口，就是問說『我是不是？』或者說『我是不是那個？』『我孩子會不會是那個？』就是『那個』、『那個』說半天，但說不出口，到底那個是哪個？很多人都說不出口。」 1998年，是台灣啟動同婚釋憲運動、也是成立第一個全國性同運團體的年份。90年代努力突破重圍的同志運動，在此時來到了與主流社會激烈碰撞的交叉點。 20年後，為因應2017年的大法官748號解釋「該如何立法」的問題，2018年台灣社會歷經一次激烈的挺同、反同公投拉扯。2019年5月17日，在幾經折衝後，台灣立法院三讀通過同志婚姻法案。從今而後，當人們愛上與自己相同性別之人，便可以自我指認為「同志」，在台灣法律體系裡有了自己的名字。 每年 6 月是全球的「同志驕傲月」，連續四天我們將一起回顧同志的相關報導。 👉請點擊連結，開啟七天免費試閱，無論是哪個年代，同性戀情一直都存在…
❤1
铁窗之后：新报告揭示中国看守所普遍存在虐待问题 保护卫士 https://safeguarddefenders.com/zh-hans/blog/behind-bars-new-report-chinas-detention-centres-shows-widespread-abuse 保护卫士今日发布最新报告指出，在中国看守所中，遭受警务人员酷刑、同仓人员施暴、律师会见受阻、监室过度拥挤以及几乎没有户外活动时间，早已成为在押人员的普遍遭遇。 问卷调查揭示了什么？ 本研究问卷由第三方机构独立编撰并执行，一共访问了84名曾被拘禁的中国公民与外籍人士。受访者合计曾被关押于19个省或直辖市、至少58处场所；如此广泛的地点分布反映出，受访者所描述的虐待与不当对待并非个案，而是普遍存在的现象。 73%的受访者表示曾被阻止会见律师 76%的受访者表示曾遭受暴力对待（施暴者包括警察、管教及其他在押人员） 76…
❤2
Repost
[Book Talk] Burnout Market Feminism - Tang Ling 唐凌 United Proud Women Time: Sunday Jun 7, 2026 06 PM PST (Western time), US Sunday Jun 7, 2026 09 PM EST (New York time), US Monday Jun 8, 2026 09 AM Beijing, China Join Zoom Meeting https://zoom.us/j/92594131096 Meeting ID: 925 9413 1096 Happy Pride Month! As we celebrate this historically meaningful and empowering season, we are especially honored to announce that P…
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纸盒分享会｜延续的节奏：华语酷儿媒介中的亲密、可见性与平台 延续的节奏：华语酷儿媒介中的亲密、可见性与平台 知和社 报名线上讲座：https://forms.cloud.microsoft/pages/responsepage.aspx?id=DQSIkWdsW0yxEjajBLZtrQAAAAAAAAAAAANAATOBzetUOU9PRllVOVNBRVlXTThUVUNBM0hOTzJWSS4u 本次分享以华语酷儿媒介（Sinophone queer media）为中心，讨论亲密关系如何在变动中的文化、媒介与平台环境中被维持、感知与延续。讲者将从连续性／连载性（seriality）与软性基础设施可见性（soft infrastructural visibility）出发，分析等待、停顿、字幕、平台更新与低强度流通如何构成酷儿生命的媒介条件。讲座也将延伸讨论情感去资格化（affective disqualificatio…
Photo, posted without a caption
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大型语言模型中中国审查偏向的分析 An Analysis of Chinese Censorship Bias in LLM Citizen Lab https://citizenlab.ca/research/an-analysis-of-chinese-censorship-bias-in-llm/ 在本文中，Citizen Lab 的 Mohamed Amed 和 Jeffrey Knockel 利用一款作为研究一部分而自行设计的审查检测工具，对大型语言模型（LLMs）中的中文审查偏见进行了审视。他们发出警告称，当大型语言模型基于受国家审查的文本进行训练时，其输出结果将更有可能与国家立场保持一致。 摘要：当大型语言模型（LLM）基于包含社会偏见的文本进行训练时，这些偏见会隐性地影响模型的输出结果。若基于“净化”后的内容——即那些经过国家审查（包括修改、删除及自我审查）过滤后所剩余的内容——来训练LLM，便会导致我们所称…
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https://linktr.ee/minjiandanganguan
脆弱的相遇：流浪吧中的酷儿政治与大都市的底层直男Precarious encounters: queer politics and dispossessed straight men on a Chinese online forum 知和社 Zhihe Society 线上讲座报名：https://forms.cloud.microsoft/r/7Qvv53HL2x 时间：北京时间4.25 14:00 本次分享基于分享人发表于Continuum: Journal of Media & Cultural Studies的论文（https://doi.org/10.1080/10304312.2026.2649545）。 本次分享聚焦在2025年10月间百度贴吧流浪吧发生的一个现象: 这个以底层直男打工人、流浪汉为主的人气社区突然涌入大量男同。走投无路、无家可归的底层直男向男同求助，而男同则以提供性或陪伴为条件伸出援手。我将这…
Showing the 12 most recent of 17 posts we hold for @sogie_webinar. 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.
Citation-graph rank — 249,892 of 1,336,469entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
Named by 1 registered channel — 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.
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.
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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“🌈 SOGIE 讲座+知识库频道 - LGBT 女权 同志 性别 多元 社运” (@sogie_webinar), 1,339 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/sogie_webinar.
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.