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说话啊上科大😕

@g0v_shanghaitech

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

614subscribers

+2 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1001418516295
TypeChannel
Username@g0v_shanghaitech
CreatedBetween 1 April 2019 and 31 October 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live15 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 15 August 2026
On Telegramt.me/g0v_shanghaitech

Growth

6126146136 August 2026 — 612 subscribers6 August 2026 — 612 subscribers15 August 2026 — 614 subscribers6 August 202615 August 2026
3 measurements spanning 9 days, net +2. 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 612–614 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
15 Aug 2026, 08:48614+2
6 Aug 2026, 17:46612no change
6 Aug 2026, 14:08612first reading

Engagement

16 posts held, back to 29 September 2024the 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.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 16 posts for this entry, the most recent from 17 May 2026. An engagement rate over an empty window would be a number about nothing.

Reaction mix

378 reactions across 16 posts, in 22 distinct kinds. The most used accounts for 25.4% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
😁9625.4%
👍5915.6%
🤮379.79%
🤡297.67%
😱256.61%
😇225.82%
🥰205.29%
🔥184.76%
🤔123.17%
112.91%
🤯82.12%
😢71.85%
👏61.59%
🫡61.59%
👎51.32%
💊51.32%
💩30.794%
🥴30.794%
😘20.529%
🤬20.529%
2 further kinds20.529%

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 16 of the 16 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 378reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 16 most recent posts we hold, published 29 September 2024 to 17 May 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

17 May 2026, 01:01 UTC≈1,520 views35 reactionsread 6 August 2026
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【新生化大楼疑似氰化物泄漏:从实验室冷库到多楼层检测异常,谁该为信息迟滞负责?】 据可靠信源信息,2026 年 5 月 7 日晚至 5 月 8 日凌晨,岳阳路 320 号新生化大楼 13 层某实验室疑似发生涉及溴化氰等氰化物相关化学品的泄漏事件。事件发生后,楼内有人员出现身体不适并前往医院就诊,部分人员被提及存在转氨酶异常、疑似肝功能或肾功能受损等情况。多份现场检测材料显示,事后新生化大楼多个区域曾进行 TVOC (总挥发性有机化合物)检测,部分点位读数异常。 根据目前掌握的可靠信源与现场材料,事件时间线可初步还原如下: 一、 5 月 7 日晚:高危试剂相关实验开展,风险源头形成 据内部信源,涉事课题组相关人员于 5 月 7 日晚开展一项涉及蛋白处理的实验,实验流程中使用或涉及溴化氰等氰化物相关化学品。该类化学品具有较高危险性,对实验条件、通风防护、废弃物处置和人员告知均有较高要求。 相关实验并未在充分风险告知和有效安全隔离的前

😱17🤯8💊5😇21😁1😢1

12 May 2026, 13:38 UTC≈1,000 views14 reactionsread 6 August 2026

【大学自 5 月 13 日起供冷】 #公共服务

👍11🥰3

18 Nov 2025, 16:14 UTC≈2,500 views3 reactionsread 6 August 2026
Sticker

Sticker, posted without a caption

😁21

18 Nov 2025, 15:31 UTC≈2,440 views47 reactionsread 6 August 2026
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【学活沦为「行政橱窗」?上科大学活新政引爆空间管理争议】 上海科技大学近期因学生活动中心管理政策急转弯,引发校内激烈争议。校方于11月7日祭出“最严管理公告”,除明令禁止打麻将外,更要求所有公共区域的使用均须提前五个工作日进行审批预约,导致学生群体强烈反弹。 据校内流出的公告及聊天记录显示,管理方以“营造文明环境”及“近期有重要会议”为由,将麻将列入与酗酒、赌博并列的禁止事项,引发日麻爱好者的不满。更为严苛的是,原先开放供学生自由休憩交流的一楼大厅及公共空间,如今均被纳入管制范围。新规要求,凡使用场馆者必须以社团或校内单位名义,而不是个人,通过“Egate”系统提前一周完成繁琐的申请流程,变相禁止了所有临时性、自发性的学生活动。 在实施方面,有来信补充,公共服务处于11.7日联合书院、学生处成立联合工作小组,安排各自下属学生(研究生驻楼兼职辅导员、书院导生)开展学生活动中心“巡楼”工作,工作内容包含“劝离”在学活进行“未报

😇20🤡15👍43🤮3😁2

14 Oct 2025, 10:26 UTC≈2,290 views39 reactionsread 6 August 2026
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【上海科大电动车新规引发关注 网上现开源车牌模板】 近日,上海科技大学自2025年秋季学期起施行电动车准入管理新规,在学生群体中引发了广泛讨论。读者来稿指出,在GitHub上发布了该校电动车通行凭证(车牌)的开源设计文件,为绕开官方申领程序提供了技术可能性。 据了解,该新规旨在“控总量、限增量、管存量”,对校内电动自行车、平衡车等代步工具实行严格的实名登记和通行凭证管理。政策规定自2025级新生起原则上不得在校内使用电动车,同时对在校生的车辆登记设置了严格的时间窗口和前提条件,且2025年8月1日后上牌的新车不予办理。 与这项严格的自上而下的管理政策相对,有技术能力的参与者选择了“自下而上”的回应方式。GitHub上的开源文件包含了制作通行凭证所需的完整设计图,任何用户均可下载。通过修改文件中的学号、二维码等个人信息,并利用在线亚克力定制服务,便可生产出外观与官方版本高度相似的实体车牌。此外,用于固定车牌的专用封条也可轻

😁29🫡6🤮3🤡1

6 Jul 2025, 04:03 UTC≈2,340 views13 reactionsread 6 August 2026
Sticker

Sticker, posted without a caption

🥰8😁3😘2

3 Jul 2025, 17:17 UTC≈2,520 views32 reactionsread 6 August 2026
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【部分信院人士收到致上海科技大学教务处及全体信息学院同学的公开信】 7月4日凌晨读者来信,指广泛信院人士收到一封关于保研政策的公开信,抗议校方突然单方面变更2024级本科生的保研办法。信中指出,校方在学生入学一年后,为计算机科学与技术专业的培养方案增添了“推免前必修”的额外限制条件。 学生认为,此举严重违反了招生时的承诺和培养方案的稳定性,损害了学生的信赖利益。公开信向校方提出八点质问,核心聚焦于:为何在未与学生沟通、且方案已执行一年的情况下,突然对特定年级(2024级)实施重大变更;为何以非正式的“口头提及”作为已通知的依据;以及该决策过程是否符合信息公开条例。 新规已打乱了许多学生基于原有方案制定的学业规划,使其面临课程安排和保研资格的不确定性。学生们的核心诉求为:要求校方正式回应质疑,保障学生平等的毕业与推免权利,并取消针对2024级学生的不合理新规。事件引发了对高校教学政策制定程序和学生权益保障的广泛关注。 本行

😁16👍142

2 Jul 2025, 17:21 UTC≈1,680 views6 reactionsread 6 August 2026
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【大学苦味受体研究涉学术争议 一作者被除名】 据论文撤稿关注网站「Retraction Watch」报道,2025 年 4 月 24 日,一篇发表在「Science」期刊的苦味受体研究论文被期刊发出勘误声明。该论文是上海科技大学作为第一完成单位发表的「Structural basis for strychnine activation of human bitter taste receptor TAS2R46」。勘误指出,原文涉及的生物发光能量转移实验存在疑虑,重复实验无法为原论文部分内容提供强有力功能支持,但核心发现仍旧具有可信度。 值得注意的是,论文原作者之一的曹晓玲( Xiaoling Cao )被指有不端行为,亦在勘误声明中从该文章除名。「Science」期刊通讯主任回复传媒查询时称,论文作者发现曹晓玲在最初的研究中「犯下不当行为(committed misconduct)」。 微信公众号「Pubpeer」报道此

🤡41😁1

2 Jul 2025, 12:47 UTC≈1,260 views24 reactionsread 6 August 2026
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上科大在今天6月28日举行了毕业典礼,祝贺各位毕业生顺利完成学业,愿你们的未来更加光明。

👍11🥰6👏5😁1😍1

15 May 2025, 07:44 UTC≈1,860 views26 reactionsread 6 August 2026

【大学自 5 月 17 日起供冷】 #公共服务

👍16😁4💩3🥴3

14 May 2025, 11:52 UTC≈1,910 views32 reactionsread 6 August 2026
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【大学学生公寓 4 号楼出现火警】 5 月 14 日下午,大学学生公寓 4 号楼出现火警。有消防车、救护车出现在大学校园。 #突发事件

🔥18😱8😢3😁2🍾1

Showing the 12 most recent of 16 posts we hold for @g0v_shanghaitech. 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

Citation-graph rank — 274,310 of 1,481,306entries 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.

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 2 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.

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 15 August 2026 — this entry's latest reading, not the date you are reading this.

“说话啊上科大😕” (@g0v_shanghaitech), 614 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/g0v_shanghaitech.

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.