Social power and information technology implementation: a contentious framing lens
Bibliographic record
Abstract
Research on the organizational implementation of information technology (IT) and social power has favoured explanations based on issues of resource power and process power at the expense of matters of meaning power. As a result, although the existence and importance of meaning power is acknowledged, its distinctive practices and enacted outcomes remain relatively under-theorized and under-explored by IT researchers. This paper focused on unpacking the practices and outcomes associated with the exercise of meaning power within the IT implementation process. Our aim was to analyze the practices employed to construct meaning and enact a collective ‘definition of the situation’. We focused on framing and utilizing the signature matrix technique to represent and analyze the exercise of meaning power in practice. The paper developed and illustrated this conceptual framework using a case study of a conflictual IT implementation in a challenging public sector environment. We concluded by pointing out the situated nature of meaning power practices and the enacted outcomes. Our research extends the literature on IT and social power by offering an analytical framework distinctly suited to the analysis and deeper understanding of the meaning power properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".