Bibliographic record
Abstract
Reply by Moyer: Pamela Fitch makes good points in response to my recent editorial on affective massage therapy (AMT), and I recall that when I was writing it, I wavered on whether to nominate AMT only as a subfield for research or as a subfield for research and practice. I also had, in addition to Fitch’s letter, a thought-provoking email exchange on that topic with Dr. Lesley Teitelbaum. Limiting my proposal to research would probably have been less controversial, but then I might have missed out on some interesting correspondence, and so I am happy to court a little controversy. Fitch expresses concern that a new massage therapy subfield could lead to a lessened appreciation for the importance of skills in therapeutic communication, empathy, and nurturance, but I disagree. I actually think the opposite is more likely. If AMT does evolve into a subfield or specialty area within the diverse profession of massage therapy, I expect that it could actually lead to an even greater understanding of and appreciation for those skills and the ways in which they interact with massage itself to bring about desired changes. Otherwise, I think that Fitch and I have much we agree on. For example, we share the belief that identifying massage therapists by the techniques they employ is problematic. In fact, this belief was one of my motivations for proposing AMT. I see the possible adoption of AMT as a shift to identification by desired outcome as opposed to identification by technique. The need for this shift parallels my experience as a psychotherapist; I found that only a few patients were curious to know about my theoretical orientation and associated techniques, but all were interested in what psychotherapy might be able to do for them. I suspect, more often than not, the same is true for massage therapy.
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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 | 0.006 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.039 | 0.065 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".