Inside the Digital Wild West: How School Leaders Both Access and Avoid Social Media.
Bibliographic record
Abstract
This study examines the roles of Canadian school leaders in response to the rising phenomenon of student use of social media which impacts school climate and safety. The use of social media has resulted in more online text and image-based communication to multiple users and less face-to-face communication with single users. Adolescent communication, a previously invigilated phenomenon, has not yet been replaced by an online social presence with a social regulation. Secondly, there have been changes in national, provincial, and district Safe School policies in response to online misbehaviour that impacts student safety within the school environment. This small study considers the views of nine Canadian secondary school vice-principals about school policies and students’ cyber behaviours. Their responses were collected on a NING, a private cyber environment. Findings indicate that when cyber events come to the awareness of the school administration, the school becomes a nexus for investigation and resolution. The study also finds that when Canadian secondary school administrators are compelled to respond to the event, these school leaders can and do access social media, employ cyber skills to identify users, intervene in wrongdoing and, in the process, follow and enact Canadian Safe School legislation. However, these same school leaders express great reluctance to use social media for their personal or professional purposes. The authors hypothesize that this may be due to their exposure to negative experiences with social media in schools.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".