Communications for the Ones Who Never Spoke : Running the MIM Marathon in the Peruvian Highlands
Bibliographic record
Abstract
How do you bring public accountability for millions of dollars to a region where the population is largely uninformed and lacks the savvy to monitor the actions of the authorities? From 2006 to 2011, the mining industry in Peru transferred over $4,774 million in royalties to municipalities located in key mining regions, in compliance with a 2004 mining canon law, but local officials have not always put these funds to the best use. With the support of Canadian, U.S., U.K. and Norwegian (through CommDev) donor partners, International Finance Corporation (IFC) responded to this need with an innovative project: Improving Municipal Investment (Mejorando la Inversion Municipal in Spanish, or MIM). MIM Peru empowers the population gives them a voice to demand accountability from their authorities in the use of royalties. For this Latin America and Caribbean (LAC) initiative, communications are essential. And the project team learned that developing effective communication is not a sprint it's a marathon! This smart lesson shares lessons learned about communications during project implementation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".