The Practice of Fixing and the Role of Fixers in Global Journalism
Bibliographic record
Abstract
Abstract Sociologists and media scholars have offered a robust body of literature regarding the daily workings of global journalism—both in newsrooms and in the field. Although fixers are sometimes mentioned in this literature, the role they play in the production of global reporting is rarely analyzed. Such work often focuses on logistical assistance provided by fixers and discusses some tensions in the field regarding credit and security. Although this literature starts to paint an accurate picture of current trends in global journalism, it fails to critically examine how institutional and on-the-ground power dynamics impact a fixer’s work, let alone how global, systemic, and institutional dynamics shape which stories are reported and how the reporting itself is done. This is a glaring gap in knowledge as it ignores the impact that fixers can have on global journalism. To rectify this gap, all aspects of global journalism must be explored, including the economic forces that allow global journalism to operate within a context of uneven power and resources. Recognizing that journalism functions in and as a field of uneven power offers a strong introduction to this discussion, but one must also situate journalism, journalists, and fixers themselves within the larger geopolitical realities of unequal economic and political power. These forces shape the process of fixing, which is why any thorough analysis of the role of fixing and fixers in global journalism must situate the conversation within a larger body of critical theory. In this context, mapping current trends and highlighting nuanced dynamics and tensions within the practice of fixing is essential to understanding how global journalism functions—and the role that fixers play in shaping its stories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.178 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.034 | 0.138 |
| Scholarly communication | 0.038 | 0.036 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".