Expanding Wilson’s information behaviour model using social cognitive theory: A case study
Bibliographic record
Abstract
Introduction. The purpose of this paper is to expand Wilson’s information behaviour model using social cognitive theory and demonstrate its use in understanding information seeking and sharing of doctoral peers in unstructured environments. Method. In this qualitative study, data was collected using twenty in-depth, semi-structured interviews of doctoral students in the social sciences and humanities disciplines. Analysis. The interview data was transcribed, imported into the ATLAS.ti software, and coded using thematic analysis. Findings. The findings demonstrate, first, that the intervening variables of the information behaviour model can fall under person and environment categories of social cognitive theory. Second, most of the variables (i.e., psychological, interpersonal/role related, source characteristics, and environment) can be found in information seeking and sharing behaviour of doctoral peers. Finally, person, environment, and behaviour factors have a reciprocal impact on one another. The environment category is discussed in this paper. Conclusion. This paper demonstrates that social cognitive theory can successfully expand the information behaviour model and make it adaptable to the context of information seeking and sharing among doctoral peers in unstructured environments.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".