Managing the Dilemma of Disclosure in Corporate Political Activity (WITHDRAWN)
Bibliographic record
Abstract
Through a longitudinal study of the macro-level formulation and organizational-level implementation of EU-level lobbying regulation. I explore what happens when lobbyists – accustomed to working in policymaking and organizational settings where covert political practices historically prevail – face new, institutional pressures to exhibit greater transparency in their work. I contribute to open-strategy literature by identifying multi-level, multi-faceted practices enacted to manage the dilemma of disclosure in the context of CPA. I also reveal societal implications and 'dark sides' of open strategy. At a purely organizational-level, lobbyists could be viewed positively as carriers of broad normative pressures, with potential to open up CPA to closer external scrutiny. Considered more critically, lobbyists’ efforts at promoting transparent practices at a higher macro-level appear to be driven by self-serving, instrumental benefits – such as improved occupational reputation, and political access – rather than pro-social motivations.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".