Bibliographic record
Abstract
In the fall of 2017, I found a series of books with the title Lands and Peoples at the University of Toronto Swap Shop. I went through the illustrations in the books and stared at the images featuring palm trees for longer than I would usually spend on any image. After that, I started to notice the consistent presence of the palm in different contexts. It was “the image” of the date palm that became the centre of my attention and that triggered memories of my personal encounters with the date palm and its fruit. Flashback to my grandfather’s burial and every funeral I have ever been to in Iran… Memories! Not only of my own, but collective memories. I remembered many stories that directly involved dates. There were flashbacks perhaps shaped by my personal memories, those of others, and from the media. I was unable to tell them apart; deep-seeded roots of history. During the winter of 2017, I gathered as much information as I possibly could about the date palm. In early spring of 2018, I bought a box of “Oriental Dates” from an Iranian market in North York, Ontario. I planted the “Oriental Date” seeds and they germinated within three weeks. A month after that, the first seed sprouted. A thousand different possibilities were shaped…
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".