The relationship between epistemic beliefs and knowledge contribution to online communities of practice
Bibliographic record
Abstract
Epistemic beliefs refer to individual beliefs about the nature of knowledge and knowing. The purpose of this study was to explore the relationship between individuals??? levels of expertise, epistemic beliefs, and their contributions to an online community of practice. The studied community was hosted by a firm and consisted of members in the design professions. Community members (N = 315) completed a self-reported survey via the Internet. Findings supported a four-factor structure of design-focused epistemic beliefs, including Consistency of Design Knowledge, Source Authority of Design Knowledge, Attainability of Design Knowledge, and Contextual Factuality of Design Knowledge. However, the last two factors had low internal consistency. Limitations of and implications for the use of the epistemic belief questionnaire are further discussed. Results indicated that individuals' epistemic beliefs could be used to explain self-reported likelihood of sharing different levels of contributions, as well as the quality and quantity of individuals??? actual contributions. Individuals with weaker beliefs in Consistency of Design Knowledge were more likely to post comments when they found a typo, disagreed with information published on a help page, found relevant tips, or wanted to share tutorials they had created. The interaction between Consistency of Design Knowledge and levels of expertise were significantly associated with the self-reported likelihood of sharing low-level contributions and quality and quantity of actual contributions. Findings are discussed with regard to their implications for both theories and designs of online communities of practice.
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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 | 0.007 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".