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Record W94219296

Critical Realism and Mechanisms: Moving from the Philosophical to the Empirical in the Search for Causal Explanations

2013· article· en· W94219296 on OpenAlex
Donald E. Wynn, Olga Volkoff, Clay K. Williams, Diane M. Strong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmericas Conference on Information Systems · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCritical realism (philosophy of perception)EpistemologyAffordanceLeverage (statistics)Mechanism (biology)Empirical researchRealismComputer scienceKnowledge managementManagement scienceValue (mathematics)Data scienceCognitive scienceSociologyPsychologyArtificial intelligencePhilosophyEngineeringHuman–computer interaction
DOInot available

Abstract

fetched live from OpenAlex

Critical Realism (CR) has recently emerged as a philosophical and methodological alternative for conducting information systems research. In order to fully leverage CR, researchers must have a clear conceptual and empirical understanding of causal mechanisms and their relationship to the organizational, social, and technological structures existing in a given research setting. Unfortunately, the mechanism concept has proved to be somewhat ambiguous. The proposed panel will address these mechanisms in general, and affordances as a specific type of mechanism which has particular value in IS research. The four panelists will discuss mechanisms from four distinct but interrelated perspectives to provide interested IS researchers with several approaches for conducting empirical critical realist research.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.188
GPT teacher head0.421
Teacher spread0.233 · 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