Bibliographic record
Abstract
Notes on Contributors The Future of Humanitarian Mine Action: Introduction K.Berg Harpviken The Convention Banning Anti-Personnel Mines: Applying the Lessons of Ottawa's Past in Order to Meet the Challenges of Ottawa's Future K.Brinkert 'Emailed Applications are Preferred': Ethical Practices in Mine Action and the Idea of Global Civil Society J.M.Beier Humanitarian Mine Action and Peace Building: Exploring the Relationship K.Berg Harpviken & B.A.Skara Balancing Risk: Village De-Mining in Cambodia R.Bottomley Risky Business or Constructive Assistance? Community Engagement in Humanitarian Mine Action B.A.Skara Acting as One? Co-ordinating responses to the Landmine Problem K.E.Kjellman, K.Berg Harpviken, A.S.Millard & A.Strand Mine Smartness and the Community Voice in Mine-Risk Education: Lessons from Afghanistan and Angola N.Andersson, A.Swaminathan, C.Whitaker & M.Roche Measures for Mines: Approaches to Impact Assessment in Humanitarian Mine Action K.Berg Karpviken, A.S.Millard, K.E.Kjellman & B.A.Skara Crisis, Containment and Development: The Role of the Landmine Impact Survey B.Eaton Making Analytical Tools Operational: Task Impact Assessment B.Goslin Ideological and Analytical Foundations of Mine Action: Human Rights and Community Impact C.Horwood
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".