Bibliographic record
Abstract
This article explores how being a small island jurisdiction affects actors in the journalism sector. The media is often referred to as the fourth estate, an institution inherently important for democracy. By scrutinizing politicians, journalists have the possibility to reveal transgressions and provide the public insight into how powerholders are performing as state officials. With this knowledge, the public can make informed decisions as to who will earn their vote in coming elections. This article studies the space for manoeuvre of investigative journalism in a small island state where the interconnectedness of people – journalists, sources, and powerholders – is a fact. It does so by studying the case of Cape Verde, a small island nation with 560 000 residents. Interviews with 12 Cape Verdian journalists from a range of the most important media outlets in the country, reveal that although freedom of expression and freedom of the press are constitutionally guaranteed, there are substantial practical limitations of free journalism. Respondents tell of widespread self-censorship, underfunding, and political interference as aspects that limit the possibility of conducting their work in a manner that would make them the watchdog institution that most of them aspire to and wish they could be.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".