A Method for Conducting Preliminary Analysis of the Nature and Context of Sport for Development and Peace Projects in Fieldwork Research: An Illustration With a Malagasy Non-Governmental Organization
Bibliographic record
Abstract
More research on sport for development and peace (SDP) organizations is needed to better understand their actual contributions to the United Nations (UN) Sustainable Development Goals (SDGs). Yet, the unstable, restricted, or even risky contexts in which many non-governmental organizations (NGOs) and SDP agencies sometimes operate often leave researchers to face important challenges to develop effective or feasible methods to work with such organizations. This study aimed to address the ontological and epistemological questions about what should be known about a given context in an organization before setting off on fieldwork. We propose a methodology, based on an actantial model (AM), as a method to analyze the nature and context of a project, to assess the actors involved in the project, and to establish if the global cost (i.e., material, temporal, financial, and physical) for conducting fieldwork is realistic and feasible of all the parties involved in the potential project. To illustrate this process, we analyzed the nature and context of an SDP project in Madagascar as the first step for potential collaborative research. As researchers, we do not want to invest time and energy to build up a fully developed field research project with an NGO in a context where it would not be realistic or feasible to conduct such research. Actually in this context, developing a research protocol without an implementation strategy might not only be detrimental to the researchers, but also to the NGO itself, where resources are often limited. Accordingly, the results from this preliminary field research demonstrate that an AM is a relevant analytical tool for obtaining insights about the context, the actors, and their relationships within an NGO. In conclusion, this model might be a useful instrument for conducting an initial analysis for the preliminary identification of the necessary conditions for the construction of a sustainable empirical research partnership with a given SDP project.
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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 | 0.103 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".