Development and validation of the Situated Academic Writing Self-Efficacy Scale (SAWSES)
Bibliographic record
Abstract
Existing writing self-efficacy instruments have assessed the concept through mechanical and process features of writing to the neglect of the influence of situated context. The purpose of this study was to develop and test the Situated Academic Writing Self-Efficacy Scale (SAWSES) based on Bandura’s self-efficacy theory and a model of socially constructed writing. A sequential multimethod approach constituted the methods. A Delphi panel of 15 expert scholars conducted a theoretical evaluation of the scale and the items were piloted with 20 nursing undergraduate students using cognitive interviews. The scale was validated in two studies with independent samples of 255 nursing students (Study 1), and in an interdisciplinary sample of undergraduate (N = 543) and graduate students (N = 264) (Study 2). The three identified factors present a structure to the questionnaire which is developmental and has the potential to detect gaps in student self-assessed ability to master various facets of disciplinary writing: 1) Writing-Essentials – synthesis, emotional control, language; 2) Relational-Reflective – relationship building with writing facilitators (teachers, academic sources) and the self through reflection; and 3) Creative Identity – exploring gaps in student achievement of transformative writing (creativity, voice, and disciplinary identity), where confidence can help identify the most engaged writers.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".