Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,014 | 0,022 |
| Communication savante | 0,016 | 0,008 |
| Science ouverte | 0,001 | 0,014 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».