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Record W4401631745 · doi:10.22215/etd/2024-16147

Defining and Resolving Contradictions in Cultural-Historical Activity Theory

2024· dissertation· en· W4401631745 on OpenAlexaff
Sherika Tamara Jackson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsCarleton University
Fundersnot available
KeywordsContradictionIdentification (biology)LoyaltyKnowledge managementManagement scienceResolution (logic)Computer scienceBusinessEngineeringEpistemologyMarketing

Abstract

fetched live from OpenAlex

This thesis develops a standard definition for contradictions and a framework for identifying and resolving them within Cultural-Historical Activity Theory (CHAT).Researchers and practitioners striving to enhance organizational efficiency and effectiveness can benefit from this framework, named the Contradiction Identification and Resolution Framework (CIRF).Synthesized from 11 studies and refined through a case study on a customer loyalty program incorporating data protection regulations, the CIRF offers a standardized approach to managing contradictions within organizational activities.Employing a ten-step research method, the thesis yields actionable insights and practical guidelines.The primary contribution lies in developing the CIRF, equipping organizations, practitioners, and management with a systematic tool to address contradictions, thereby facilitating improved decision-making, streamlined processes, and enhanced organizational performance.The nature and practical applicability of the CIRF are expected to have a positive impact on organizational practices, fostering favourable outcomes across various sectors.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.356
Teacher spread0.337 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

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