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Administrative Communications Among School Leaders and Special Education Teachers During COVID-19

2023· book-chapter· en· W4386334848 on OpenAlexaffabout
Pei-Ying Lin

Bibliographic record

VenueAdvances in logistics, operations, and management science book series · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicImpact of Education Environments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsThematic analysisCoronavirus disease 2019 (COVID-19)Special educationPedagogyMedical educationAdministration (probate law)School administrationPsychologyPolitical scienceSociologyQualitative researchMedicine

Abstract

fetched live from OpenAlex

The chapter explored administrative communications from schools, divisions, and the province as perceived by special education teachers in Saskatchewan, Canada during school closures. Through one-on-one online interviews and a thematic analysis, the present study investigated how and how often school leaders communicated with school staff, and what they communicated about, as well as what special education teachers were concerned about regarding administrative communications across the board. Most staff meetings were held virtually on a weekly basis with one exception: meetings were held infrequently at one federal school. Teachers felt that these meetings helped facilitate the discussions among teachers and school leaders about online teaching and learning. Ineffective communications between administration at different levels was a concern for teachers because some communications were delayed and insensitive to the needs of students and their families as well as teachers. Finally, educational implications for crisis leadership were also discussed in this chapter.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.252
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.071
GPT teacher head0.389
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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