Strangers in the Dark: Navigating opacity and transparency in open online career-related knowledge sharing
Bibliographic record
Abstract
Given repeated upheavals in jobs and organizations, people increasingly share career-related knowledge in open online platforms. Dealing with career-related knowledge in an open online setting, though, is challenging. It requires people to balance between exchanging too much and too little career-related knowledge, e.g., to disclose and share the right knowledge without jeopardizing themselves. This study examines how participants achieve such delicate balance in open online processes. It investigates discussions in a career advice-focused online platform. Findings reveal how open online career-related exchanges include sequences of knowledge sharing, knowledge evaluating, and of diverting. They also include sequences of regulating openness that involve securing opacity for the people participating while also ensuring the transparency of the process. The study unpacks how participants in an open online setting navigate the dynamic balance between individual opacity and processual transparency. Findings hold implications for scholarship on open organizing, careers, and advice networks, as well as for 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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".