Bibliographic record
Abstract
[Conference Program Abstract] Failure to educate all students has long-term implications for student attainment of educational,social,economic and cultural aspects of education. Current education literature reveals a gap between what is expected in the school system and how teachers are trained to meet diverse student learner needs. Hence, the decision to investigate the following for my doctoral dissertation: What is the experience of novice teachers who teach secondary school students with diverse learning needs? Pertinent and relevant information exposed in the research findings permitted the following themes to emerge: The Calling; Hiring Process; Pre-service Teacher Preparation; Defining Novice Teacher Roles and Responsibilities; Mentorship and Continued Professional Development; and One-Size-Fits-All Model Does Not Fit Diverse Learner Needs. Several questions that surfaced from the participants‘ detail regarding the impact that inadequate preservice preparation played on their daily roles and responsibilities as: Who am I and what do I teach; How do I teach; Who helps me teach; and How effective is my teaching style? In conclusion, the research study provided a deeper understanding of each participant‘s experiences. The information revealed a somewhat contentious, isolated, and frustrating role while experiencing satisfaction in taking a stance for what and whom they believe in: success for Ultimately, each participant expressed the desire to have their stories presented with the hope that their experiences can be beneficial at all facets of the education system while discovering what novice teachers experience in a day and how they can be assisted.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".