Value and Values in the Interstices of Journalism and Journalism Studies: An Interview with Candis Callison and Mary Lynn Young
Bibliographic record
Abstract
In this interview, Professor Candis Callison and Professor Mary Lynn Young, along with MEDIA INDIGENA podcast creator Rick Harp, provide a deep and sometimes personal set of insights as to why the field of journalism studies came to function the way it did and why that field so often falls short in its analysis of issues related to race, indigeneity, gender, and colonialism. Both Callison and Young highlight the arguments they make in their recent book, Reckoning: Journalism's Limits and Possibilities, about the role and practice of journalism as it relates to methods, ideals, aspirations, social order, and ethics. They conclude with a discussione of the theoretical and epistemological frameworks that undergird their analyses in the book, and address the tensions between value and values in the news.
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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 | 0.034 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.051 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.027 |
| 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".