Learning More Effectively from Experience
Bibliographic record
Abstract
Developing the capacity for individuals to learn effectively from their experiences is an important part of building the knowledge and skills in organizations to do good adaptive management. This paper reviews some of the research from cognitive psychology and phenomenography to present a way of thinking about learning to assist individuals to make better use of their personal experiences to develop understanding of environmental systems. We suggest that adaptive expertise (an individual's ability to deal flexibly with new situations) is particularly relevant for environmental researchers and practitioners. To develop adaptive expertise, individuals need to: (1) vary and reflect on their experiences and become adept at seeking out and taking different perspectives; and (2) become proficient at making balanced judgements about how or if an experience will change their current perspective or working representation of a social, economic, and biophysical system by applying principles of "good thinking." Such principles include those that assist individuals to be open to the possibility of changing their current way of thinking (e.g., the disposition to be adventurous) and those that reduce the likelihood of making erroneous interpretations (e.g., the disposition to be intellectually careful). An example of applying some of the principles to assist individuals develop their understanding of a dynamically complex wetland system (the Macquarie Marshes in Australia) is provided. The broader implications of individual learning are also discussed in relation to organizational learning, the role of experiential knowledge for conservation, and for achieving greater awareness of the need for ecologically sustainable activity.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".