Bibliographic record
Abstract
OBJECTIVES: The purpose of this commentary is to explore the concepts underpinning professional identity, assess their relevance to chiropractic, and propose a model by which a strong identity for the chiropractic profession may be achieved. DISCUSSION: The professional identity of chiropractic has been a constant source of controversy throughout its history. Attempts to establish a professional identity have been met with resistance from internal factions divided over linguistics, philosophy, technique, and chiropractic's place in the health care framework. Consequently, the establishment of a clear identity has been challenging, and the chiropractic profession has failed to capitalize on its potential as the profession of spine care experts. Recent identity consultations have produced similar statements that position chiropractors as spinal health and well-being experts. Adoption of this identity, however, has not been universal, perpetuating the uncertainty with which the public regards the chiropractic profession. CONCLUSION: To gain public and professional acceptance, chiropractic must be unequivocal in declaring its scope, expertise, and intent. Failure to do so will lead to obscurity as other professions acquire necessary skills and position themselves as the custodians of spine care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".