Texts, Lies, and Mediascapes: Communication Technologies and Social Media as Risk in the Educational Landscape: Des médias sociaux en tant que risque dans le paysage éducatif
Bibliographic record
Abstract
Studies have shown how digital communications impact administrators’ work, but few have looked at the reputational risks to school administrators incurred through social media and digital communications. This Alberta case study looks at risk through Kasperson et. al’s (1988) social amplification of risk framework for an exclusion room controversy. Twitter responses are analyzed and interpreted over a longitudinal, 5-year period. Despite school administrators’ perceptions that risk might be generated on social media from community-led, grass-roots sources, traditional figures and agencies such as provincial news media and politicians appear more influential than school administrators, teachers, or parents in the Twitterverse. Implications are drawn for educational administrative behaviour and policy.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".