Institutional Work In Community Based Forestry Management: An Ethnography of Dialogue and Translation Performed In An Indigenous Peoples’ Organization
Bibliographic record
Abstract
Among forestry and development scholars and practitioners, threats to forest environments may be mitigated with the participation of indigenous people in Community Based Forest Management (CBFM). The paradigm of CBFM among practitioners and theorists is based on models of common resource management that depend on institutionalization, where communities formulate rules of resource management through communication. Critics of the post-positivist inclinations of CBFM have argued that discourse and humanistic approaches can provide a better understanding of effective CBFM. Neo-institutional theories had been proposed that provide such a humanistic understanding. Specifically, this dissertation proposes that organizational discourse analysis under a paradigm of communicative institutionalism can provide researchers with a deep understanding of the complexities of CBFM. Using theories that blend neo-institutionalism, postcolonial theory, and Montreal School theories within a Communication as Constitutive paradigm, it is theorized that processes of dialogue and translation are a form of institutional work, as transformative practices used by indigenous peoples’ organizations (POs) in the construction, distribution, and delegitimation of texts. Texts here are theorized as having agency and that they constitute, along with other (human) agents, an institutional field. The author engaged in six months of ethnographic data gathering in the form of interviews, participant observation, and document collection. Through organizational discourse analysis of the data, it was found that (1) the institutional work of dialogue and translation produces hybridized indigenous texts through modalities of sensemaking, legitimation, and intertextuality; (2) efforts to resolve discursive tensions among different discourses and texts present lead to the production of hybridized institutional texts; and (3) how through the authoring of hybridized institutional texts, intertextual relationships are reconstituted in ways that reconfigure the institutional field.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".