Bibliographic record
Abstract
In the aftermath of the school shootings in Newtown, Connecticut this past December, we experienced the world around us as less safe—understandably so. In response to such a tragic event, there is a degree of fear instilled in all of us that for many was at its peak in the New Year as we prepared to send our children back to school. The fear we experienced can be considered as both a cause and an effect. It is an effect in that it is an emotion brought about by a perceived threat; it is a cause in that reactions can be ascribed to it. School Boards across the United States and Canada reacted to the threat to school safety by investing in increased security strategies such as surveillance cameras, on-site police officers and security guards, enhanced lockdown procedures (all school entrances and classroom doors are locked at all times), and regular lockdown drills much like the fire drill practices with which we are all familiar. In Ontario, Canada for example, where I practiced for many years as a school-based child and youth care worker, the provincial government reacted by declaring that 10 million dollars would be immediately allocated to implement a “locked door” policy to enhance school security measures. All this is intended to protect our children from threat or harm and to create a safe environment in which children can play and learn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.073 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".