Manifestations of corporate social responsibility as sensemaking and sensegiving in a hydrocarbon industry
Bibliographic record
Abstract
Abstract There is a large body of literature that examines different dimensions of corporate social responsibility (CSR) in Africa, with many focusing on the false promises of these corporate initiatives. Contrary to simplistic claims of CSR being merely window‐dressing, however, this paper reveals that although several rhetorical proclamations underpin the idea, such statements are often given instrumental meaning through diverse mechanisms (e.g., interpretation of cues toward the proactive (re)construction of identity, (inter)subjective discourses on social legitimacy, and acts of “issue selling”) that help to enact particular characteristics of the corporation. The paper specifically employs the organizational concepts of sensemaking and sensegiving to explain how, through CSR activities, hydrocarbon companies in Ghana construct and (re)affirm a particular reality for its stakeholders. The findings suggest that by having significant leverage over the (re)construction of its identity and claims around social legitimacy and performance, the corporation gives sense to and further sustains its authority over societal norms and expectations around what social responsibility entails. The evidence presented contributes to scholarship that considers the corporation as a complex nexus of multiple relations, contested narratives, and practices.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".