Bibliographic record
Abstract
This chapter offers a comparative look at the ways in which countries and international organizations framed spaces of intervention at local, departmental, (sub-)regional or national scales. It explores which spaces were prioritized and how they were labelled with explicit negative and positive references. Broadly, negative comments included the presence of armed conflict, criminality and illegal actors and economies. International actors also highlighted HR violations against segments of the population, gender inequality and violence and the presence of anti-personnel mines. Inequality was also foregrounded, particularly between urban and rural areas, and the latter's lack of civil access, poverty, poor agricultural productivity and water management, low environmental resilience, deforestation, lack of economic opportunities and investment and weak governance, state services and infrastructure. Positive comments included Colombia as a model of democracy, innovation and leadership regarding the peace process post conflict. The country's social capital was also praised as entrepreneurial, diverse, culturally rich and resilient, and with social organization capacities regarding peace. Colombia's status as an upper-middle-income country was also highlighted, as well as its urban development, great biodiversity and economic potential for carbon capture, exploration, tourism, agro-industry, oil, mining and energy extraction, and economic investment. The chapter shows that, first, positive and negative comments were related to international actors’ agendas, as geographic scales were treated more as containers of issues and comments were similar across different scales. Secondly, major cities such as Bogota, Medellin or Cartagena were more positively framed than smaller cities (Barrancabermeja, Tumaco, San Jose del Guaviare, Mocoa), municipalities and rural areas (Statista, 2020). The latter were treated as spaces with little governance, a presence of illegal economies and violence, and that were in need of development. Such characterization signalled development preferences and reinforced a normative duality between rural and urban spaces, as well as a commitment to those that could functionally link and interact in international markets as a precondition of peace. Regarding positive depictions of major cities, the exceptions were Canada, who identified violence and criminality within city areas, and MAPP/OEA, who identified the vulnerable zones in Cali (in Valle del Cauca), for example. Positive comments were also directed to broader geographic regions in terms of their biodiversity, investment and development potential; as well as to spaces for reincorporation of ex-combatants, which facilitated both economic development and reconciliation.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".