Bibliographic record
Abstract
When someone asks me how or why I became a writer, I hesitate before answering.Should I tell the truth or should I reply with a superficial but socially acceptable answer?I never wanted or planned to be a writer.In the 50s and 60s when I attended elementary and high school, I dreaded the subject "Composition."Year after year, we were drilled in spelling, grammar rules, and the three elements of essay writing: introduction, body / main points and conclusions.Our teachers chose the subject matter.Whenever I needed to commit words to the page, my imagination either presented me with too many alternatives or I drew a blank.Usually I produced short, terse paragraphs.I rarely received a failing grade, but came close with marks that hovered in the "C" range.My essays were returned to me with numerous red circles indicating spelling and grammar errors.Typically, my teachers passed back marked essays starting with the pupil receiving the highest mark.I felt embarrassed my classmates also knew about my below average grades.When I attended university, my essay grades improved, mainly because of my detailed research.Research appealed to me because I loved reading.When I was a child, we did not have books at home.At age seven, I discovered the Dundas Carnegie Library.There, the librar-
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".