Bibliographic record
Abstract
Abstract Colonial powers used electronic media and communication technologies to assert and extend control over spaces as well as attempt to influence the “hearts and minds” of colonized people, colonial settlers, and Europeans in the metropole. Colonized people adapted and repurposed these technologies, often toward anticolonial ends. In the early mid-19th century, the telegraph effectively became the “nervous system of empire,” collapsing distances and enabling colonizers to surveil and dominate colonized people and institutions from the metropole (with varying degrees of success). In the early 20th century, new media forms like wireless radio were used to “educate” and “civilize” colonial subjects, entertain and relieve the anxiety of settlers, and spread propaganda in the colonies and the metropole about the benefits of imperialism. These technologies helped to build both deliberate and accidental, colonial and anticolonial, transnational networks. Some of those networks assisted in anticolonial political mobilizations, particularly in India, where the telegraph was accessible to the public and facilitated nationalist organizing, and Algeria, where radio helped to galvanize support for the revolutionary FLN. Postcolonial media landscapes hold the histories of colonial power asymmetries; we see present-day continuities in the concentration of ownership of media and communication technologies among racial and economic elites, and in the Eurocentrism of dominant regimes of representation.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".