Operationalizing knowledge coevolution: towards a sustainable fishery for Nunavummiut
Bibliographic record
Abstract
Knowledge coevolution is the process through which information is generated by joining knowledge systems in an inclusive and iterative way to facilitate self-determination of communities and promote cultural resilience. A central and practical component of this framework is the fostering of progress towards improved co-management and community led research. Here, we illustrate a knowledge coevolution framework in the context of a major five-year genomics and food security fishery research project in Gjoa Haven, Nunavut. We highlight the process, changes in research objectives, logistical requirements, mutual benefits, and challenges associated with northern collaborative research, and what lessons we have learned from the process. Knowledge coevolution could be linked to more inclusive and effective fishery co-management in Nunavut and possibly elsewhere. Further, the research process appears to have reinforced Indigenous knowledge and Western science without merging these distinct knowledge systems. Here, we strive to provide readers with concrete examples of knowledge coevolution and encourage research groups to incorporate and improve these practices in future projects and in adaptive fishery co-management. We further call on funding agencies to place more value, and thus budgetary priority, on activities related to ongoing consultation, engagement, dissemination, and implementation of project outcomes. Qaujimaningi maruk ajingingituk pivallianirijanginnik pigiarutauplunni tukisikanirutit saqipaliasurmata nunalit nangminiq aulajungnaliquplugit iliqusinginniglu saqipalliatitsiquplugit aulajungnalirlutik nangminiq. Qitianittuq amma ilulirijauplunni piliriangujup pivallianiq turangajuq aqiumakanirnirmut aulatauninganut amma nunalingnut aulataujuq qaujisarniq. Ukua tava takutijutauniaqtut qaujimaningi maruk ajingingituk pivallianirijanginnik pilirianguniaqtunnik ilulinginnik angijut aragunni tallimani qaujisarnirmut amma niqiqatiarnirmut iqalulirinirmi qaujisarniq Uqsuqtummi, Nunavummi. Nalunaiqsipluta pilirianguniaqtunnik, asiangurninginnik qaujisarniup iluanni, aturiaqaqtunik piqutinik, angiqatigingnirmi, amma ilautittinirmik ukiuqtaqtumiunik qaujisarnirmut, iliniataujunik piliriarmit. Qaujimaningi maruk ajingingituk pivallianirijanginnik ilinganajarmat aulatauninganut kajusitiarnirmullu iqaluliriniup mianirijauninganni Nunavummi asinginnilu. Amma suli, qaujisarniq aqiktausimaplunni nunaqaqqaqsimajut tukisianinginnut amma qaplunat tukisianinginnut katitausimangiłutik tapkuak ajingingituk qaujimaniujut. Uvanni piliriaqaqpugut tukisikaniquplugit uqalimaqtut tungaviqatiaqtummik ukturaulaujunik qaujimaningi maruk ajingingituk pivallianirijanginnik amma ikajuqtuipluta timisiujunnik qaujisaqtinik atausingurlutik aqigiarlutiglu piliriangusuqtut sivunirmi amma atuliqtitaulutik iqalungnik aulatauninganut. Tuksiralaurrapta kinaujannik aturniaqtunnik timiujunnit, sivulliutitauplutik qinaujat aturiaqaqtut kajusijummut uqaqatiqarnirmut ilautittinirmut, asinginnullu tusaqtittinirmik atuliqtittinirmullu piliriangujunnik.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".