Bibliographic record
Abstract
Contemporary disability legislation often includes addiction in the category of disability. The choice to exclude addiction from this legislation can create unnecessary barriers for those people with addictions. Exclusion of addiction from legislation may block an asffected individual from being able to access benefits or services which could potentially help them succeed. This paper applies Schneider and Ingram's (1993) model of social construction to an understanding of the issues surrounding the inclusion of addictions in disability legislation. With inclusion of addictions in disability legislation, people with addictions are viewed in a more positive light, both by themselves and by society at large. This more positive construction would both identify and assist in engineering a shift in societal perceptions. As well, inclusion potentially increases access for affected persons to benefits and services. Increased access would likely help to destigmatize addiction and therefore meaningfully support people who seek overcome them. Logistical complexities inevitably arise in considering the creation of an accessible society for people with addictions or substance issues, but contemporary studies suggest that inclusion in legislation would have effects that are more positive than negative.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".