Bibliographic record
Abstract
A 65-year old farmer and forestry worker in Quebec’s Eastern Townships, John Buzzell, kept a diary from March 1916 until December 1918. Although in the third year he lived in the urban setting of Paris, Ontario, it is a typical example of a rural work diary, recording the weather, listing details of his workday, and describing family’s and neighbours’ activities and health. Such journals furnish social historians with rich detail about rural life but little about the emotions and sensibilities of their authors. This research note proposes and uses two systematic practices, or tools, for addressing the well-known analytic difficulty of recognizing the non-dit and extracting emotion from rural work diaries. Focusing on literary and physical conventions of composition and structured comparison, these two practices can unpack emotions such as sorrow, pride, pleasure, nostalgia, anxiety, and loneliness and help lift the veil of discretion about public and family affairs. Many other kinds of journals or memoirs similarly contain little explicit expression of emotion, and the two tools described in this research note may open more sources and archives, as well as lowering the risk of selecting on the dependent variable, choosing for the study of emotions only diaries that include their unmediated expression.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".