Self-Talk: An Interdisciplinary Review and Transdisciplinary Model
Bibliographic record
Abstract
The present work synthesises the self-talk literature and constructs a transdisciplinary self-talk model to guide future research across all academic disciplines that engage with self-talk. A comprehensive research review was conducted, including 559 self-talk articles published between 1978 and 2020. These articles were divided into 6 research categories: (a) inner dialogue, (b) mixed spontaneous and goal-directed organic self-talk, (c) goal-directed self-talk, (d) spontaneous self-talk, (e) educational self-talk interventions, and (f) strategic self-talk interventions. Following this, critical details were extracted from a subsample of 100 articles to create an interdisciplinary synthesis of the self-talk literature. Based on the synthesis, a self-talk model was created that places spontaneous and goal-directed organic self-talk as well as educational and strategic self-talk interventions in relation to variables within their nomological network, including external factors (e.g. task difficulty), descriptive states and traits (e.g. emotions), behaviour and performance, metacognition, and psychological skills (e.g. concentration).
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".