Bibliographic record
Abstract
As I older, I find that I dislike arguments more and more, especially when the structure predetermines who gets in the last word. Therefore, I've resisted, I hope, the temptation to set the record straight, or get in the last word, and instead focused on the messages being communi cated in my paper and those of the discussants. After reading the reac tions of the reviewers, and rereading my own manuscript, I have two predominating thoughts: one pertaining to the main reason I wrote the paper in the first place and the other stemming from the specific com ments of the discussants. One of the main reasons for writing my article was to raise the profile of evaluation in counselling. It certainly seems to have done that. Nancy Hutchinson (1997 [this issue]) links the ideas in my paper to a literature base that reflects her own work. She provides excellent examples of how recent practices in the field of education can contribute to the develop ment of a research base to inform evaluation practices in counselling. (I hope that others will join me in addressing the challenge offered at the close of Hutchinson's commentary.) Bob Flynn (1997 [this issue]) pro vides a very nice summary of the main arguments in my paper. (It is always humbling to see one's work boiled down to a few words, especially when it's done accurately.) He then takes the opportunity to focus on aspects of evaluation that he thinks are important. Richard Young (1997 [this issue]) takes many of the points I tried to make, recasts them using constructivist language, and elaborates the necessity of maintaining fidelity with stakeholder perspectives and expectations. (It seems that Young's comment has more to do with the language I use than the arguments I am trying to make.)
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 | 0.134 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.019 | 0.125 |
| Scholarly communication | 0.049 | 0.053 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".