Bibliographic record
Abstract
Abstract: Effective learning is viewed as an evolutionary process, and as such, it involves an expanded version of the Crenshaw-Collins view of intersectionality. It demands an in-depth view of the complex socio-cultural-ethnic milieu in which students are embedded. Even more, effective learning requires effectance problem-solving, investigation and semiotics, along with effectance motivation, to form a quadripartite framework for effectance holism, which becomes the foundation for equity. Equity in the classroom requires shared human experience, research, process, ideas, as well as product. Effectance motivation associates walking, awareness, attention, perception, thinking and adapting to one’s environmental conditions that encourage effective, competent interactions of students with their surroundings. Arguably, effectance, rather effective , motivation is evidentiary in childhood development, and is responsible for acquisition of increased intellectual awakenings in the home and in the classroom. However, effective motivation alone is self-limiting. I include effective problem-solving, investigation and semiotics into the equation. That students are active, constructive participants in the learning process is also evidentiary. With Susan Harter effectance motivation encompasses the developing intellect of children and evolution of their independence, mastery, competency and success. Against this background of scholarship research, Gardner’s multiple intelligences portray student success and motivation as a pathway only to stereotypical roles, without any educational value. In contrast, egosystem provides a viable framework for understanding students and their complex makeup. In fact, I argue that frames of reference should replace frames of mind . In terms of the value of learning through the arts, early modernism, especially Dada and Surrealism, have inspired students to reimagine their own art as having, not only intrinsic aesthetic value, but also extrinsic narrative value as social-political commentary. Essentially, art and design education must reimagine what students could do, if only they did not have to conform to a set curriculum, and were allowed to research art history on their own, explore their personal passions and experiment with various art forms.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".