Theorizing Improved NLP Features for Promoting Behavior that Supports CMC Users’ Subjective Well-Being
Bibliographic record
Abstract
This work-in-progress identifies gaps in the current Natural Language Processing (NLP) approaches for pro-social communication detection by organizing the state-of-the-art NLP feature detection approaches according to models of Subjective Well-Being (SWB) from Positive Psychology. We need to better understand the current state of the field and what features of prosocial computer-mediated communication (CMC) we have yet to address. Théoriser les caractéristiques améliorées du NLP pour promouvoir un comportement qui favorise le bien-êtresubjectif des utilisateurs de communcation virtuelle RésuméCe travail en cours identifie les lacunes dans l’approche actuelle de détection de la communication pro-social en organisant les approches de détection des caractéristiques de l’état de l’art NLP selon les modèles du bien-être subjectif (Subjective Well-being, SWB) en psychologie positive. Nous devons mieux comprendre l’état actuel du domaine et quelles caractéristiques de la communication prosociale assistée par ordinateur (computer-mediated communication, CMC) n’ont pas encore été abordées. Mots-clésbien-être subjectif; processus de langage naturel; communication prosociale assistée par ordinateur
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".