New Frontiers of Philanthro‐capitalism: Digital Technologies and Humanitarianism
Bibliographic record
Abstract
Abstract Digital technologies that allow large numbers of laypeople to contribute to humanitarian action facilitate the deepening adoption and adaptation of private‐sector logics and rationalities in humanitarianism. This is increasingly taking place through philanthro‐capitalism, a process in which philanthropy and humanitarianism are made central to business models. Key to this transformation is the way private businesses find supporting “digital humanitarian” organisations such as Standby Task Force to be amenable to their capital accumulation imperatives. Private‐sector institutions channel feelings of closeness to aid recipients that digital humanitarian technologies enable, in order to legitimise their claims to “help” the recipients. This has ultimately led to humanitarian and state institutions re‐articulating capitalist logics in ways that reflect the new digital humanitarian avenues of entry. In this article, I characterise this process by drawing out three capitalist logics that humanitarian and state institutions re‐articulate in the context of digital humanitarianism, in an emergent form of philanthro‐capitalism. Specifically, I argue that branding, efficiency, and bottom lines take altered forms in this context, in part being de‐politicised as a necessary condition for their adoption. This de‐politicisation involves normalising these logics by framing social and political problems as technical in nature and thus both beyond critique and amenable to digital humanitarian “solutions”. I take this line of argumentation to then re‐politicise each of these logics and the capitalist relations that they entail.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.069 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".