Bibliographic record
Abstract
I learned to authentically connect by observing the pedagogues who mentored me. My lived experience with them inspired me to base my pedagogical approach on the constructs of community and engagement that youth dismantled by displaying increasing disengagement, which transferred into disaffected relationships. This reflexive/narrative autoethnography investigates the problematic phenomenon affecting youth: the loss of authentic connectivity. I critically examine my professional journey with pre-digital, digital, and post-digital university students by analysing our common, cultural context, thereby interpreting my behaviour, thoughts, and experiences in relation to them. Hermeneutic phenomenology’s framework deepens the inquiry, as it involves a broader cultural, political, and social understanding to uncover deeper meaning in changing behaviours by reflecting on what is the lived experience of authentic connectivity for youth. My comprehensive research evidences that youth’s technological addiction has influenced rapid brain evolution, and exploded their visual and multimodal skills. Neuroscience has broadly concluded that the new forms of learning technology offers are changing the way the brain processes information. I suggest that youth are experiencing a biological conflict, the brain’s rapid evolution overwhelming more slowly evolving physical responses, effectively interfering with the flow of affective information that requires hemispheric transfer. Neither moving beyond the premise of intelligence as being predominantly brain-based, nor acknowledging the cooperative role our bodily intelligence plays, as the latter is embedded in our lived experience, the greater understanding of the whole of learning, and its ally, authentic connectivity, cannot be achieved. I submit that moving beyond the absoluteness of a purely scientific approach to the brain, and integrating both human and cognitive sciences are key in moving toward a more holistic, autonomous learning pedagogy, so to layer our understanding of the ‘person process’, that which includes whole thinking and whole being. To counter the affective devolution, which is detrimental not only to learning, but to being a well-adjusted person, this paper proposes a foundational shift in teacher training curriculum design by suggesting tools that foster an observational pedagogy, which seeks to teach those navigational skills that support higher-level analytical processes that can counteract the excessive reactions that impede learning, and teaching.
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How this classification was reachedexpand
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".