Curriculum-Based Ecosystems: Supporting Knowing From an Ecological Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The goal of this article is to advance an ecological theory of knowing, one that prioritizes engaged participation over knowledge acquisition. To this end, the authors begin by describing the environment in terms of affordance networks: functionally bound potentials extended in time that can be acted upon to realize particular goals. Although there may be socially agreed-upon trajectories specifying the necessary components of a network activated for realizing a particular goal, the particular network engaged by an individual is dependent on adopted intentions and available effectivity sets, the attunements and behaviors that an individual can enlist to realize an affordance network. Thus, to help clarify the challenges of connecting learners to ecological systems through which affordance networks are activated, the authors use the term life-world, which refers to the environment from the perspective of an individual. Building on their characterization of affordance networks, effectivity sets, and life-worlds, the authors offer an ecological focal point for curricular design.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.001 |
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 it