Bibliographic record
Abstract
The purpose of this article is to deconstruct the narrative of Sunshine sketches of a little town (LEACOCK, 1912) as to make out what might be hidden in-between the jokes told by its narrator. Stories do not only tell us things objectively nor linearly, but are successively asking us to reflect upon what they are not effectively saying. This is what allows our reconsideration about our relationship with meanings external to us – the meanings “we have not seen coming”. Translating would be, thereby, analogous to some sort of reverse time travel: the journey from my nowhere to the direction of the nowhere of the other. If the local colour of Leacock’s (1912) fictional town, Mariposa, is what makes it unique, to generalise its features would be a mistake; regardless of his narrator's assertions, Mariposa is not synonymic to every Canadian town. I am not trying to argue here nonetheless that the local has no relevance to the global, or vice versa; my point is that one does not need to imply the absence of the other, it is their correlation that must be restored.
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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.101 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| 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".