Bibliographic record
Abstract
Craig Johnson is an Associate Professor in Political Science. His research lies in the field of international development, focusing primarily on the ways in which global demand for land, resources and energy is affecting patterns of poverty, climate vulnerability and environmental sustainability in the Global South. Between 2009 and 2013, he led an international team of researchers looking at the socio-economic and environmental implications of urban land acquisition in India, Bangladesh and Viet Nam. He is now taking forward new work on the global race for alternative energy sources, particularly in the oil and gas sector. Finally, he is editing a book that will be published with Routledge in 2015 about the ways in which cities around the world are now responding to the global climate challenge. David MacDonald is a Professor in Political Science. His research connects Canada and New Zealand. Canada and Aotearoa New Zealand are located on opposite sides of the world, yet both countries are grappling with how to forge better relationships between settlers, indigenous peoples, and ethnic communities. How a country is imagined and represented can make a difference. Canada’s bilingualism and multiculturalism both symbolically alienate First Nations, Metis, and Inuit peoples, whose unique historical and legal status is often ignored. In New Zealand, the dominant narrative is biculturalism – a partnership between indigenous Maori and Paheka (European settlers). Ethnic communities do not easily fit into this image of the nation. His research examines the ways in which imagining community affect how these three groups form alliances or compete with one another for recognition and resources.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.138 | 0.043 |
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".