Bibliographic record
Abstract
The contributors to the first half of this collection are human service educators and practitioners whose articles focus on software they have developed for work with youth and adults. These articles were selected for inclusion in this edition for the following reasons: The designers/authors had produced software (text based mostly) for human services purposes in the past and so their current work represents the progress that technology has made in our field. These designers/authors are the second stage innovators whose work needs to be recorded. These programs are being used by professional helpers and are actually employed in various human service programs to support practice (software programs described in the first edition were mostly demonstration models). Some of the software programs included had been subjected to increasingly rigorous and formal evaluations, the outcomes of which indicate the utility of properly designed software in our profession. They represent the internationalization of technology in the human services as well as the interconnectedness of our field. The software described in these articles were developed, tested and utilized in Australia, Canada, Israel, Finland, the United Kingdom and the United States. These programs are now more user friendly, employing more sophisticated technology to create well-designed software for a variety of uses: a play-therapy tool, a child welfare aid, an educative/preventative intervention for a variety of issues and as a problem-solving program in a school setting.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".