Conversations on Critical Thinking: Can Critical Thinking Find Its Way Forward as the Skill Set and Mindset of the Century?
Bibliographic record
Abstract
The capacity to successfully, positively engage with the cognitive capacities of critical thinking has become the benchmark of employability for many diverse industries across the globe and is considered critical for the development of informed, decisive global citizenship. Despite this, education systems in several countries have developed policies and practices that limit the opportunities for students to authentically participate in the discussions, debates, and evaluative thinking that serve to develop the skill set and mindset of critical thinkers. This writing examines the status of critical thinking in four different contexts across the globe as reflected in educational policies and academic experiences as a preface to investigating actual classroom practices and possible impacts the support of critical thinking skills may have on the potential development of the global citizens of the future. Each vignette reflects the contextualized difficulties that are presented by social and cultural concerns and traditions of making meaning. These stories of education also illustrate the various ways in which the skills and capacities of critical thinking are interpreted in different contexts and address the negative nuances with which thinking critically has become associated. Finally, a pedagogical model of teaching, which may support student development of the skill set of critical thinking within the boundaries of social and cultural mindsets, has been developed.
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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 | 0.046 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.093 |
| Scholarly communication | 0.034 | 0.043 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".