Mutual Transparency or Mundane Transgressions? Institutional Creeping on Facebook
Bibliographic record
Abstract
This article explores the post–secondary sector’s adoption of social media. Social media plays an increasing role in the visibility of personal information. The term refers to a set of web–based services that facilitate the authorship and distribution of media between users. The most popular social media site is Facebook with over 750 million users worldwide. Facebook was originally launched as a service for university students to author and distribute information about their personal identity, interpersonal connections, and social activities. While Facebook has since expanded its scope beyond universities, student life remains a heavily ‘Facebooked’ phenomenon. University administrators are keenly aware of their students’ presence on this site, and are adopting new practices to harness Facebook as an extension of their professional duties. This paper draws upon findings from a series of fourteen semi–structured face–to–face interviews with various administrators and employees at a medium–sized university in Eastern Ontario. As Facebook first emerged in an academic context, these findings provide a rich example of how institutions can scrutinize populations using social media. These findings suggest that institutional surveillance on Facebook stems from ground–up practices prior to implementing top–down mandates, suggesting that these practices have developed from institutional users’ personal experiences with the site. As well, the visibility of the university and its reputation is offered by respondents as motive for scrutiny, suggesting a discourse of mutual transparency of both the university as an institution as well as its student population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".