Reply to “Do We Always Practice What We Preach? Real Vampires’ Fears of Coming Out of the Coffin to Social Workers and Helping Professionals.”
Bibliographic record
Abstract
This reply analyzes criticism of the article “Do We Always Practice What We Preach? Real Vampires’ Fears of Coming Out of the Coffin to Social Workers and Helping Professionals” published in Critical Social Work (2015), 16(1) by DJ Williams and Emily E. Prior. That article was widely publicized and received a seemingly disproportionate amount of criticism from both religious and secular voices. This reply applies Peter Berger’s notion of anomie to suggest that critics of the article felt threatened by the implications of tolerating emerging identity claims, such as those made by self-identified vampires. By attacking Williams and Prior as unreasonable, these critics suggest that an individual’s ontological status is taken-for-granted rather than socially constructed. Paradoxically, their protests also suggest an awareness that ontological status actually is socially constructed and that helping professionals, such as Williams and Prior, are imbued with cultural authority that can alter the established order. This reply suggests that the ontological threat presented by helping professionals is what is actually at stake in these critiques. Critiquing the article appears to be not only a call for the continued medicalization of self-identified vampires as deviant, but more importantly a strategy of repressing the realization that norms are socially constructed and therefore susceptible to change.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.054 | 0.050 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".