Prepare to be Offended Everywhere: How Cultural Safety In Public Places Can Prevent Violent Attacks
Bibliographic record
Abstract
The greater mobility of people and the worldwide displacement of millions, forced or voluntary, has directed attention to their human security in foreign environments.We propose that a focus on cultural safety is becoming an essential requirement for the human security of displaced minorities everywhere, building on previous work in educational settings [6].As a necessary though insufficient condition for human security, cultural safety is affected by dependency relationships, power imbalances, dominant paradigms, and norms of public conduct as they apply to displaced cultural minorities.The central question how cultural safety could be enhanced is addressed through empirical scenarios where the cultural safety of individuals was placed in jeopardy.Such events often manifest as the perception of offence, sometimes leading to violent conflict.Recognizing the futility of attempts to prevent all and any offence, our arguments amount to a novel approach to strengthen cultural safety, and thus human security: preparing both sides for offensive experiences as a means to pre-empt counterproductive reactions.We discuss strategies toward that goal that might allow individuals, families, larger groups and organizations to work collaboratively towards ensuring the cultural safety of displaced people, thus making a vital contribution towards sustainable human security.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".