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Record W43926797 · doi:10.15173/mjc.v1i0.220

Assessing the Level of Administrative Censorship and Control in Student Newspapers in Ontario

2004· article· en· W43926797 on OpenAlexaffvenueabout
Kristin Wozniak

Bibliographic record

VenueThe McMaster Journal of Communication · 2004
Typearticle
Languageen
FieldComputer Science
TopicDigital and Cyber Forensics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNewspaperCensorshipControl (management)Political scienceSociologyMedia studiesComputer scienceLawArtificial intelligence

Abstract

fetched live from OpenAlex

In the now famous documentary Manufacturing Consent, Noam Chomsky (1992) differentiates between the structure of the professional and student press. Chomsky argues that the professional press is governed by an elite body, whereas the student press is not. In fact, Chomsky notes that the student press is often ignored as a media source unless it takes steps to radically break medial and societal conventions. It is only then that the student press feels pressure from the authoritative class. In the professional press, it can no longer be disputed that the media is under the close watch of the authoritative class, and subsequently, the media is often censored. Whether it is a silenced profanity in a prime-time Hollywood movie, or the complete exclusion of opinion regarding a controversial news issue, the pubic rarely gets to see the full picture. The underlying question regarding censorship is, what is the motivation? The answer is painfully simple: profit and influence (Bagdikian, 1992). News media in particular are susceptible to very specific types of censorship. Owners want to influence their audiences and profit from them. And to ensure that their goals are met, owners and publishers pay great attention to the content and slant of the news, because if the public doesn’t tune in, the owner loses both money and potential influence. University publications, on the other hand, are run on a different set of goals and values. The goals of student publications are not profit and influence, but information and education. Because the goals are different, the process, ownership, and organization of the newspaper are inherently different. Many university publications receive funding from either the university administration directly or from another university source such as a students’ union. And “although salaries and news production costs often are paid by administrators, few believe that there is a correlation between funding and news selection” (Bodle, 1994, p. 907). But is this true? How much control does the funding body of a student publication have over content and slant? This study will aim to address these questions by examining the level of administrative control and censorship in student newspapers across Ontario using David Taras’ ownership model, and Noam Chomsky and Edward Herman’s propaganda model, as the defining theoretical frameworks. Four primary research questions are going to be considered: (1) How often and why does the funding body (excluding advertising revenue) attempt to control the content of the student publication, and how successful are they? (2) Under what circumstances do editors-in-chief or executive editors of university publications feel pressured, either directly or indirectly, by the funding body to censor or tailor the content of the newspaper, and under what circumstances do editors oblige? (3) From the editor’s point of view, how does the funding body handle situations in which unfavourable content has been published in a university publication? (4) What do editors see as the prime function of the student press? What measures are taken to ensure that this mandate is fulfilled?

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0060.004
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.148
GPT teacher head0.342
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes3
Has abstractyes

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