Indigenous participation in peer review publications and the editorial process: reflections from a workshop
Bibliographic record
Abstract
This communication paper reflects on discussions from a workshop about Indigenous involvement in the peer review and editorial processes. Arctic-based research is undergoing a paradigm shift to include local Indigenous Peoples, their priorities, and knowledge throughout the research process. This special issue is an excellent example; it highlights research involving partnerships between Indigenous and non-Indigenous researchers to support knowledge co-production. Despite this shift, we find little space within the standard peer review and editorial processes for Indigenous Peoples, their perspectives, and knowledge. To discuss this issue, we organized a half-day workshop at the 2019 ArcticNet Annual Scientific Meeting with a diversity of Indigenous and non-Indigenous participants who are involved in Arctic research. The discussions revealed that answering questions about the involvement of Indigenous Peoples in the peer review and editorial processes largely begins by addressing the challenges of achieving equity in the research process generally. Our discussions demonstrated that further conversations are needed and that no single approach will work in all cases, but that there are several concrete actions that researchers, universities, funding organizations, and publishers can take to begin addressing this issue. Taanna tusaumaqatigiguti paippaaq uqausiqarmat uqallaqatigigutaulauqtunik katimasinnaarutiqaktillugit Nunaqaqqaaksimanirmut qaujisattiarnirmik qimirrulutik ammalu aaqqiksuqtautiuqtillugit pilirianguningit. Ukiuqtaqturmittuq qaujinasuarvik pilirivalliajuugaluaq tukisinarutaugajuktumik piliringaaliqpallialutik piqasiujjauqullugit nunalinni Nunaqaqqaaqsimajut inungit, ammalu qaujimaningit iluunnalimaangani qaujinasuarniup pilirianguningata. Taanna ajjiungittuq akaunngiliuruti piujuaalungmat tukisinaqsitittijjutauninga; ujjirnaqsitittingmat qaujinasuarnirmik piliriqatautittininganit piliriqatigiignningitigu kamakkua Nunaqaqqaaksimajut ammalu uqqurmiut qaujinasuaqtit ikajuqsuiqullugit qaujimanirmik sanaqataujunik. Tamannaugaluatillugu piliriangungaaliqpallianinga, nanisigatta piviqarvigalaangannit iluani atuqtaulluatasuni qaujisattiarluni qimirrunirmi ammalu aaqqiksuigiakkannirnirmut pilirinirmik Nunaqaqqaaksimajut inungnut, kiggaqtuijinginnut ammalu qaujimaninginut. Uqaqatigigutiginiarlugit tamakkua akaunngiliurutit, aaqqiksuilauratta avvanganit ulluup katimasinnaarnirmik taikani 2019 Ukiuqtaqtumik Tukisiniaqatigiit Arraagutamat Qaujinasuarnirmuungajunik Katimaqatigigniq ajjigiingillutik Nunaqaqqaaksimajut ammalu Nunaqaqqaaksimajuungittut piliriqataujut taikkua piliriqataujut Ukiuqtaqturmi qaujinasuarnirmi. Uqaqatigingniit saqitittilaurmata tamanna kiuqattarniq apiqqutinik turaangajunik piliriqatautitauninginnut Nunaqaqqaaksimajut inungit qaujisattiarluni qimirrunirmi ammalu aaqqiksuigiakkannirnirmi piliriniujunik angijumik pigiarutiqasungumat piliriangunasuaalirninginnut piliriangujarialiit pijaunasuarutauluni taimaalluaqatigiingnirmit qaujinasuaqtut pilirininginni tamaitigut. Uqaqatigignivut tukisinaqsitittingmat tauvungakkanniq uqaqatigigutiqakkanniriaqaratta ammalu pitaqangimmat atausiarluni pilirijjutaugajaqtumik aaqqiksijjutaugajaqtumik qanuittutuinnarni piliriangujuqarajaqpat, kisiani qatsikallangnik sanngijunik pilirigiarutaujuqarmat qaujinasuaqtikkunnit, silattuqsarvigjuanit, kiinaujaqaktittijit iqanaijarviqunginnit ammalu uqalimaagaliuqtit pilirigiarunnarmata tamanna pilianguqullugu akaunngiliuti.
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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 | 0.233 | 0.355 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.053 | 0.032 |
| Scholarly communication | 0.033 | 0.020 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".