A Protocol for When Social Media Goes Private: Studying archaeological or heritage discourses in closed Facebook groups
Bibliographic record
Abstract
Our major project explores the discourses that surround the buying and selling of human remains over social media. We discuss the research ethics framework established in Canada by the 'Tri-Council' research agencies as it pertains to studying social media in general. Issues of privacy and consent are paramount. Human remains trading happens in both public and private social media. We detail the process we went through, and the protocol that we evolved as a result, for studying private social media posts in closed Facebook groups. This process, protocol, and rationale may be useful for other researchers studying how archaeology and cultural heritage are framed or discussed in these venues. What people say in public is not what might be said in private, and researchers need ethical approaches to study such discourses.
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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 | 0.213 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".