Who Contributes Knowledge? Core-Periphery Tension in Online Innovation Communities
Bibliographic record
Abstract
Where do valuable contributions originate from in online innovation communities? Prior research provides conflicting answers. One view, consistent with a community of practice perspective, is that valued knowledge contributions are primarily provided by central participants at the core of a community. In contrast, other research—including work adopting an open innovation perspective—predicts that valuable ideas primarily emerge from peripheral participants, those at the margins of a field of knowledge who provide novel ideas and viewpoints. We integrate these contrasting perspectives by considering two distinct forms of position: social embeddedness (a core social position within the social network of participants interacting within a community) and epistemic marginality (a peripheral epistemic position based on the network of topics discussed by a community). Analyzing contributions by 697,412 participants of 52 Stack Exchange online innovation communities, we find that both participants who are socially embedded and participants who are epistemically marginal provide knowledge contributions that are highly valued by fellow community participants. Importantly, among epistemically marginal participants, those with high social embeddedness are more likely to provide contributions valued by the community; by virtue of their epistemic marginality, these participants may offer novel ideas while by virtue of their social embeddedness they may be able to more effectively communicate their ideas to the community. Thus, the production of knowledge in an online innovation community involves a complex interaction between the novelty emanating from the epistemic periphery and the social embeddedness required to make ideas congruent with existing social and epistemic norms.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".