Bibliographic record
Abstract
Over the past four years I have received many letters from individuals interested in submitting an article and have also received requests for information from several Promotion and Tenure Committees regarding our process. I have responded accordingly. Teaching and Learning is a nonprofit professional publication started in 2002 by myself, then Associate Dean of the Faculty of Education and the Brock-Golden Horseshoe Education Consortium which consists of ten school boards and the faculty of education, Brock University located in the greater Niagara Region of Ontario Canada. The publication is designed primarily to enhance the professional development of teachers and others interested in education in schools and communities. The topics for each issue are determined by an executive board and are decided at an annual meeting. Each board and the faculty contribute financially to the publication. And receive 600-700 copies of each issue three times a year. Subscribers contribute to the funding as do occasional selected advertisers and sales of single and multiple copies. The publication is perhaps more of a professional periodical and newsletter than a "formal" scholarly journal (meaning "blind" reviews, although that said most articles other than interviews and solicited lead articles are vetted by selected readers). The publication combines elements of both a professional publication and a journal and attracts authors and readers from all segments including applied research. Our readership is local, national and international. Articles are received in a variety of ways. They are usually in response to a formal call for papers printed in previous issues. In addition the editor solicits articles from key professionals and from time to time interviews selected individuals who have contributed significantly to the focus of the issue. Articles are received by the editor and read for content, relevance, appropriate writing style and clarity of thought and articulate communication of what is important in terms of the professional development interests highlighted in the issue at hand. Articles are then sent to selected readers (who have volunteered to adjudicate but who wish to remain anonymous) for readability and to determine if the article would be appropriate for inclusion in Teaching and Learning. I f the article is deemed acceptable then authors are advised and the editor works with the authors and the publisher to edit the article to fit the space, style and format of the publication (81/2x11, 3 column format 32-36 pages). This is a collaborative venture that involves communicating with authors and the publisher over several draft revisions and it is one that seems to work well .
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 | 0.054 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.062 | 0.022 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.181 | 0.361 |
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".