The Effects of Noun-Labelling by Others and the Self in the Domain of Mental Disorders
Bibliographic record
Abstract
Using noun phrasing to refer to an individual's maladaptive behavioral pattern (e.g., John is a drinker.) may lead to stronger inferences of identity, compared with non-noun phrasing (e.g., John drinks). Building on research from developmental and social psychology, the current studies examine the impact of noun labels in the mental disorder domain. In Study 1, 171 undergraduate participants read descriptions of hypothetical individuals’ behaviour (e.g., gambling, drinking, overeating) phrased using either noun labels or non-noun phrasing, depending on the condition randomly assigned. The hypothesis that participants would rate behaviours described using nouns as more stable and resilient compared with behaviour described using non-nouns was not supported. Self-labelling was investigated in the Study 2, 167 undergraduate participants were randomly assigned to either a drinking or gambling condition. In response to a series of questions regarding which of two phrases would reflect greater amenability to change, participants chose between a noun-label phrase (e.g., “I am a gambler”) or a non-noun equivalent (e.g., “I gamble whenever I can”). As predicted, participants’ perceived the noun-label phrase (e.g., “I am a drinker”, “I am a gambler”) as more in-keeping with an intent to change. These findings broaden our understanding of the effects of language which implies identity in the domain of mental disorders. Discipline: Psychology (Honours) Faculty Mentor: Dr. Andrew Howell
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".