LEAVING HOME TO COME HOME: REBUILDING JOURNALISTIC GATE-KEEPING ONE FACT AT A TIME
Bibliographic record
Abstract
Confusion about modern post-publication fact-checking is dissipating as the practise nears its 20th anniversary in the United States (where it began in earnest), and misinformation settles as a resistant and sometimes deadly drag on reality. But a nuanced analysis of fact-checking’s role in and on the journalistic field (Bourdieu, 1993, 1998; Benson & Neveu, 2010) is troubled by such independent characterizations of post hoc claim verification as: a revolt against journalism; a professional or social reform movement; a new genre; an entrepreneurial exercise; a status-seeking ploy; and/or a psychologically ineffective or damaging reinforcement of falsehoods. Given that accuracy is an ethical requirement of normative journalism and the defining characteristic of ‘news’( hence the political duplicity of the oxymoronic term, ‘fake news’), the birth of independent fact-checking troubles the practice of journalism and, by proxy, political knowledge, and can thus also be read as critique of the field writ large. While gatekeeping (Lewin, 1947) is assumed dead, and gate-watching (Bruns, 2003) on life support, modern fact-checkers are nevertheless culling and privileging information, while simultaneously adjudicating and assigning blame for public speech, which traditional ‘neutral’ journalism avoids. Using both both academic and journalistic qualitative and quantitative interview tools, and framed by field, gatekeeping/watching, and discourse theories (thus emulating the journalistic-academic hybrid model deployed in fact-checking), I examine the largely unexplored area of journalistic re-entrenchment and introduce the reverse-gate-keeping theory of ‘information corralling’ in the era of escalating misinformation.
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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 | 0.121 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.030 | 0.101 |
| Scholarly communication | 0.054 | 0.041 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".