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Record W2272415037

Anonymity in Behavioural Research: Not Being Unnamed, But Being Unknown

2006· article· en· W2272415037 on OpenAlexaff
Jacquelyn Burkell

Bibliographic record

VenueScholarship@Western (Western University) · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsWestern University
Fundersnot available
KeywordsAnonymityOperationalizationScrutinyEmpirical researchContext (archaeology)Variety (cybernetics)Identity (music)IdentifierPsychologyRelevance (law)Action (physics)Social psychologyCognitive psychologyInternet privacyData scienceEpistemologyComputer sciencePolitical scienceArtificial intelligenceComputer security
DOInot available

Abstract

fetched live from OpenAlex

EMPIRICAL RESEARCH IN THE SOCIAL SCIENCES should help answer a crucial question: how does anonymity influence behaviour? A quick perusal of the literature, however, reveals that the answer provided by this research is far from simple. According to the empirical literature, “anonymity” has broad, varied, and inconsistent behavioural effects. A deeper reading reveals that the complexity of behavioural effects is matched by the complexity and variety in the empirical definitions of “anonymity.” Analysis of empirical manipulations designed to operationalize the concept reveal that they reflect three distinct concepts: 1) identity protection (withholding of name or other unique identifiers); 2) visual anonymity (being unseen by communication partners); and 3) action anonymity (where the content and even existence of actions are unavailable to others). The first of these manipulations closely matches the traditional definition of anonymity, while the second and third relate more to being known (visually or by one’s actions) than to being identified. Thus, in the context of behavioural research, anonymity is defined in two intertwined ways: as lacking unique identifiers and as being hidden from public scrutiny. LA RECHERCHE EMPIRIQUE EN SCIENCES SOCIALES devrait aider à répondre à une question clé : quel est l’effet de l’anonymat sur le comportement? Un bref survol de la documentation révèle, toutefois, que la réponse qui se dégage de ces recherches n’est guère simple : les effets de l’anonymat sont nombreux, variés et incohérents. Une lecture plus attentive révèle que la complexité des effets de l’anonymat sur le comportement est comparable à la complexité et à la diversité des définitions empiriques du terme « anonymat ». L’analyse des manipulations empiriques visant à en opérationnaliser le contenu démontre qu’il existe trois concepts distincts : 1) la protection de l’identité (la dissimulation du nom ou d’autres identificateurs uniques); 2) l’anonymat visuel (la préservation de l’invisibilité aux yeux des partenaires en communication); et 3) l’anonymat des actes (la dissimulation aux autres à la fois du contenu des actes et des actes mêmes). La première de ces manipulations correspond à peu près à la notion traditionnelle de l’anonymat. La seconde et la troisième ont trait davantage à la connaissance de l’individu (visuellement ou par ses actes) qu’à son identification. Par conséquent, dans le contexte de la recherche sur le comportement, l’anonymat comporte deux définitions entrelacées : l’absence d’identificateurs uniques et la protection du soi contre l’examen public.

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)0.104
metaresearch head score (Gemma)0.238
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.548

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1040.238
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0060.069
Scholarly communication0.0110.020
Open science0.0020.009
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.253
GPT teacher head0.417
Teacher spread0.165 · 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 designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations8
Published2006
Admission routes1
Has abstractyes

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