SOCIOLOGICAL RESPONSIVENESS AND ADJUSTMENT AS A TOOL FOR SOCIAL DEVELOPMENT AND PUBLIC SETTLEMENT- CANADIAN PERSPECTIVE
Bibliographic record
Abstract
THE COLLAPSE OF the notable twin towers of New York's World Trade Center in 2001 influenced Canada in a larger number of courses than the loss of two dozen Canadians among the unfortunate passings on that day. Instantly, American fingers indicated as far as anyone knows remiss northern outskirt security that had encouraged terrorist activity in the United States. In spite of the fact that this creation was in the long run recognized all things considered, the harm was finished. The characterized circumstance was genuine in its outcomes. In a bitingly humorous move, Canadian authorities exceeded themselves in showing security cautiousness with the outcome that few pure Canadian natives were whisked away by American constrains in remarkable version, to endure torment in Syria and Egypt and to have their lives shredded by the encounters. The best known of these is Syrian-conceived Maher Arar, an Ottawa architect (see O'Connor 2006). In every circumstance, the misusing of individual data relating to the casualties was vital to their wrongful detainment. In spite of the fact that security and observation are verifiably fundamental to this circumstance, with some striking special cases (Calhoun 2002) sociological examination did not figure unequivocally in endeavors to comprehend it.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.029 | 0.076 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".