L’écriture abrégée dans la prise de notes d’étudiant·e·s postsecondaires au début du 21<sup>e</sup> siècle : comparaison d’échantillons de notes manuscrites et dactylographiées
Bibliographic record
Abstract
Introduction: In an academic context, abbreviations allow students to compensate for the difference between talking speed and writing speed (handwritten or typed) while taking notes. The difference between talking speed and writing speed (handwritten or typed) suggests that handwritten notetaking may require more abbreviation strategies to compensate for its slower speed. Objectives: This study aims to compare the use of abbreviated writing in typed and handwritten notes, and to analyze the frequency of different types of abbreviations in these two notetaking modes. The main hypothesis is based on the idea that handwritten notes have a higher rate of abbreviations than typed notes because of the greater time constraint. Method: The corpus is composed of twenty-two samples of post-secondary lecture notes in French, of which eleven are handwritten and eleven are typed. Four types of abbreviations were analyzed: logograms, siglas/acronyms, aphereses/apocopes, and syncopations. Welch’s t-tests were used with and without outliers to determine the significance in differences in abbreviation frequency according to the notetaking mode. Results: Statistical tests indicate a significant difference between both notetaking modes for total abbreviation frequency (p=0.043, d=0.97 with outliers, d=1.15 without outliers). Excluding outliers reduces the mean deviation of abbreviations but reinforces the significance of the difference. T‑tests show that aphereses/apocopes are used significantly more frequently in handwritten notes (p=0.019, d=1.25), while the respective frequencies of syncopations and logograms approach significance (p=0.056 and p=0.061). Conclusions: Handwritten notes show a higher proportion of abbreviations than typed notes, probably due to a more marked time constraint. The results also confirm strong inter-individual variability.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".