Issues in deafness: an analysis of the existing literature pertaining to issues of the cultural-linguistic definition of deaf culture.
Bibliographic record
Abstract
"Ask yourself, "What would it be like to be deaf?" When hearing people are asked to consider deafness, they most likely try to imagine themselves in a world of silence. As a first-year undergraduate, I was part of a discussion with several of my friends in which we were asked to decide, given only the options of being deaf or being blind, which we found to be the lesser tragedy. I announced that given only those two fates, I would choose blindness for myself. To never hearing music, the voices of my loved ones, to be deprived of the ability to speak, to carry on a simple discussion as we were having then, was simply unbearable to me. Such a decision was made based on my limited capacity to conceive and imagine such an existence as a world of silence. It is people such as this, who little consider deafness and only then in the intermittent moments when it is absolutely necessary, who are bestowed the power to enact policy and decide practice for the deaf. It is not in fact the deaf themselves who, though infinitely better suited to understand and interpret their own experiences and needs into policy and practice, are permitted to do so. As will be explained throughout this essay, there are specific reasons for the position of deaf people in Western society which manifest themselves politically, economically, and culturally."--Page 1.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".