Mapping Populations: The United Nations, Globalization, and Engendered Spaces, 1948–1960
Bibliographic record
Abstract
Over the last hundred years, we in the more favored parts of the world have been doing all that we could to displace [the] attitude of resignation and feeling of inability to do anything about such circumstances [“poverty and disease and ignorance”]. We sent missionaries throughout the world preaching the gospel of the Fatherhood of God and the brotherhood of man and converting a lot of people in the underdeveloped areas of the world to a conviction that they can in fact work out an improvement of their own lives. In addition to these religious missionaries we sent out trade missionaries, commercial agents, who aroused desires on the part of the people of the underdeveloped areas for conditions of life and physical comforts that are commonplace in the advanced countries of the West. Moreover, during both World Wars, but particularly during the second, we sent our military forces into practically every corner of the world so that at the present time there is no place anywhere on the globe in which any considerable number of peoples live who do not know that it is possible for a human being to live a far better life than is customary for three quarters of the human race.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".