Bibliographic record
Abstract
This paper explores corporate charitable giving disclosures in order to question the extent to which corporations can claim that their philanthropy activities are charitable at all. Exploration of these issues is carried out by means of a tropological analysis that focuses on the different linguistic tropes within the philanthropy disclosures of 52 companies, namely metaphor and synecdoche. The results reveal a number of complex and contradictory things. Primarily, the master metaphor of ‘altruism’ projected by the corporate disclosures is ideologically at odds with the more business case‐oriented discourse that shapes the disclosures. This contradiction is put into starker contrast by the existence of a root metaphor, whereby the recipients of corporate philanthropy are presented as the ‘deserving poor’. Synecdochal devices are present within the corporate disclosures, whereby employee initiatives that are independent of corporate strategies are used to confer attributes onto the disclosures that bolster the master metaphor of ‘altruism’. As such, corporate philanthropy is presented by the paper as a structurally incoherent discourse and yet one that has implications for both extracting greater value from various societal groups and in defining, on behalf of civil society, what is a worthy cause.
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 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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".