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Record W2091734178 · doi:10.1657/1938-4246-42.4.379

Commentary. Integrating Research, Education, and Traditional Knowledge in Ecology: a Case Study of Biocomplexity in Arctic Ecosystems

2010· article· en· W2091734178 on OpenAlexaboutno aff
William A. Gould, Grizelle González, Donald A. Walker, Chien‐Lu Ping

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachValue (mathematics)Environmental educationMainstreamScience educationPopulationEducational researchSociologyPolitical scienceEngineering ethicsPublic relationsEcologyPedagogyEngineeringBiology

Abstract

fetched live from OpenAlex

Integrating research and education is a fundamental goal of institutions and agencies supporting science because of the benefits to society of a more informed and scientifically literate population. The value of engaging public interest in ecological research is to maintain support for and integrate science in solutions to environmental problems (Hudson, 2001; Avila, 2003). The National Science Foundation lists criteria for assessing broader impacts of research projects which include (1) the integration of research and education—advancing discovery and understanding while promoting teaching, training, and learning; and (2) developing opportunities to broaden the participation of groups underrepresented in science (NSF, 2006). Educational researchers are developing models and assessing outcomes of integrating research and education at diverse grade levels (Trautmann and Krasny, 2006; Bowen and Roth, 2007); the value of integrating traditional ecological knowledge (TEK) in research has been demonstrated in several ecosystems (Huntington, 2000; Kimmerer, 2002; Ford et al., 2007; Garcia-Quijano, 2007) but specific approaches and achievements of efforts integrating research and education are not widely disseminated in environmental research journals. Thus, while there is a call for environmental scientists to broaden their activities to engage in outreach (i.e., have broader impact) there is a lag in the assessment of the effectiveness of these activities and of their value in mainstream scientific culture. Environmental scientists seldom evaluate these impacts, and there are few venues or incentives to report on these activities in ways that would enhance their research careers. For an individual scientist, efforts expended in integrating research and education often occur at the expense of research productivity and this results in a lack of reward for a researcher’s efforts to broaden research impacts (Andrews et al., 2005; Uriarte et al., 2007). One way to address the imbalance between efforts devoted to broader impacts vs. avenues for reporting on these efforts is through the publication of case studies and assessments of integration efforts in journals that reach a research audience as opposed to an education audience. This venue exists in a very few, high-profile, broad-interest research journals (e.g., Science, Bioscience) but could be more widespread in journals addressing a range of environmental research. Examples of successful integration help researchers and institutions evolve better mechanisms to achieve goals beneficial to society, including improved public understanding of science, greater diversity of research and stakeholders, and better application of current scientifically based information to managing environmental issues. In that spirit, we present as an example an effort integrating an interdisciplinary research project investigating the interactions of climate, vegetation, and permafrost in the study Biocomplexity of Arctic Tundra Ecosystems with a university field course, Arctic Field Ecology, and with indigenous Inuit students and elders. The integration allowed university students and native community members to participate with the research team, drawn by the opportunity to gain education and experience. This participation has had synergistic benefits with the research agenda and diversified the pool of stakeholders involved in the research (see Box 1).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.188
GPT teacher head0.401
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2010
Admission routes1
Has abstractyes

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