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Record W2895538834 · doi:10.1177/1476127018803255

Strategy and dialectics: Rejuvenating a long-standing relationship

2018· article· en· W2895538834 on OpenAlexaff
Moshe Farjoun

Bibliographic record

VenueStrategic Organization · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsYork University
Fundersnot available
KeywordsDialecticCoherence (philosophical gambling strategy)EpistemologyPerspective (graphical)Construct (python library)Field (mathematics)Generative grammarConversationSociologyProcess (computing)Computer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

This essay broadens the conversation on the state of organizational contradictions and paradox research by turning to dialectics—a time-honored, living perspective on social processes and relations, which continues to influence our understanding of the past, present, and future. Dialectics distinctive relational process worldview sets it apart from approaches stressing equilibrium, linearity, and coherence, making it highly relevant to a world in flux. I propose that dialectics is already present in strategy research and in contemporary business, and can become even more central to strategy, addressing core questions in the field and propelling it in new directions. Strategy scholars can draw on dialectics principles as a generative tool kit to construct new theories and managerial tools. Dialectics can also be used as a theoretical lens to understand emerging empirical phenomena such as the rapid advent of artificial intelligence. Finally, dialectics critical stance and philosophical grounding makes it a particularly attractive perspective for challenging existing theoretical models and for considering alternatives.

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)0.018
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0070.081
Scholarly communication0.0210.043
Open science0.0020.013
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.238
Teacher spread0.200 · 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.

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

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

Citations30
Published2018
Admission routes1
Has abstractyes

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