Bibliographic record
Abstract
This short paper outlines an ethnographic project exploring how influence, reputation, and academic identity are circulated and enacted within scholarly online networks. Both academia and social networks can be said to be ‘reputational economies’ (Willinksy, 2010), but while scholars and educators are increasingly exhorted to ‘go online,’ those who do often find that their work and efforts may not be visible or understood within institutional contexts. This project utilizes ethnographic methods and a material-semiotic theoretical approach to explore and detail the ways in which networked scholarly reputations operate, circulate, and intersect with contemporary concepts of academic impact. The study aims to articulate the signals which ‘count’ towards influence and scholarly reputation in networked circles, and to explore the benefits and challenges that networked scholarly participation poses for contemporary academics who engage in it. Research into computer-based interactions has, for decades, suggested that online group members develop signals for status and credibility: Walther (1992) found “electronic communicators have developed a grammar for signalling hierarchical positions” (p. 78). More recently, Kozinets (2010) framed this status differentiation less in terms of hierarchy than “various strategies of visibility and identity expressions” (p. 24). Literature on networked scholarship is growing but has not as yet delved deeply into questions of how networked reputations, credibility, and status positions are produced, nor what implications these hold for conventional academic practices. This research investigates reputational strategies and practices within networked publics from a new literacies perspective, as a form of networked learning with the ethos of participatory culture. The paper explores the contexts, understandings, learning processes, and mediating technologies that have contributed to the development of participants’ outlooks and specific practices. Likewise, it also frames those practices and outlooks in relation to multiple circulating concepts of influence that intersect within academic networks. Through interviews and extensive participant observation within scholarly online networks, this project explores how interactions within scholarly networked publics intersect with conventional notions of academic identity, and offers a snapshot of the various ways in which online networks open up new possibilities for scholarly engagement, learning, identity expression and influence that may not be visible, legible, or available within the academy.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.024 | 0.051 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".