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Record W2022095654 · doi:10.3138/jsp.38.1.1

Plagiarism, Publishing, and the Academy

2006· article· en· W2022095654 on OpenAlexvenueno aff
Dan Harms

Bibliographic record

VenueJournal of Scholarly Publishing · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicPublishing and Scholarly Communication
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingMisconductPublic relationsAmbivalenceOrder (exchange)DishonestyIdentity (music)Set (abstract data type)SociologyAcademic dishonestyPolitical scienceMedia studiesLawBusinessPsychologyHigher educationSocial psychologyComputer science

Abstract

fetched live from OpenAlex

While plagiarism claims have skyrocketed, the response within academia to scandals among its members has remained ambivalent. While most people are willing to proclaim plagiarism as a serious offence, our actions when confronted with cases among our colleagues often vary considerably. While this has been going on, mergers, tough financial times, and the growing quest for short-term bestsellers have transformed the publishing world, both on a broader scale and within academic publishing in particular. This has created a situation in which the goals of publishers and those of academia with regard to intellectual dishonesty have diverged considerably. Several recent examples are described in which misunderstandings have developed regarding the role publishers play in maintaining scholarly integrity. The author also describes his own experience, in which a publishing company chose an explanation geared more toward its own interests than to that of scholars when handling a report of plagiarism. Calling attention to these events should not be perceived as demonizing publishers and blaming them for misconduct. Rather, uncertainty within the academy makes it easy for those outside it to render academic judgements irrelevant and to set their own policy. Instead, academics should begin a candid discussion on the importance of maintaining or altering plagiarism rules in order to have a stronger and more unified voice capable of more influence on outside parties, whether students, corporations, or media. Note: No information will be given regarding the identity of the publishing company or the author involved in the incident mentioned.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScholarly communicationResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptScholarly communicationResearch integrity
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.010
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0260.024
Scholarly communication0.0350.014
Open science0.0020.012
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0160.004

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 head0.035
GPT teacher head0.230
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

Labeled directly by 2 models reading the full record.

Scholarly communicationResearch integrity

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
Domainnot available
GenreEmpirical · Other

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

Citations10
Published2006
Admission routes1
Has abstractyes

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