Capability and Vulnerability: A Discourse Analysis of Multi-Jurisdictional Emergency Planning Documents
Bibliographic record
Abstract
Background: Deaf people constitute a minority group; most deaf people use sign language, which is not universal.In emergencies and disasters, conditions are created that can affect their lives.The role of a sign language interpreter in emergencies can be vital.In Israel, there are no regulations concerning the work of the interpreters in emergency situations.Despite that there are about 250 registered interpreters of Israel Sign Language, only around 120 professional interpreters work.Methods: A cross-sectional survey of 84 interpreters of sign language in Israel was carried out.A self-administered questionnaire was developed, inquiring into various aspects of willingness to work in emergency situations, including translator-client interactions and translators' work characteristics.Results: The majority of respondents live in the central region of Israel (79%), 83% of them are women.Only 45% of interpreters work full-time.Thirty-seven percent of the respondents are hearing children of deaf parents.Half of them stated that in emergency situations, they need to help a relative before working as an interpreter.Conclusion: Significance of the findings: In an emergency, there might not be enough sign language interpreters.Most interpreters are women who do not work full time, and are committed to care for their family first.Most emergency situations in Israel have occurred in periphery areas of the country, where a small number of interpreters live.These findings point to inequities in emergency situations, not only towards the deaf community, but also among different groups in the deaf community.Recommendations: Encourage interpreters of sign language to study and work in periphery areas, granting financial incentives to study professional interpretation, and to work during emergencies.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".