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Record W333470459

Supporting New Teachers

2007· article· en· W333470459 on OpenAlexaboutno aff
Steve Metz

Bibliographic record

VenueThe Science Teacher · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationCensusPovertyEconomic growthIrishSociologyMathematics educationPolitical sciencePsychologyDemographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

The city of Lawrence, Massachusetts, has a wonderful diversity, originally serving as a turn-of-the-century entry point for Irish, Polish, Italian, Syrian, and French-Canadian textile workers, and now attracting newcomers from the Dominican Republic, Puerto Rico, Vietnam, and dozens of other countries. Hispanics now make up almost 70% of the population (U.S. Census Bureau 2005). Like most cities, Lawrence faces significant challenges. A dwindling manufacturing base, and subsequent loss of manufacturing jobs, has created a city where over one-third of the population is below the poverty line. Unemployment, while declining from 15% in the 1990s, still remains at 10%, over twice the national average (EOLWD 2005). Lawrence schools face many of the same issues as other urban schools in the United States, including a reported dropout rate of 40% that is almost four times the reported state average (MDE 2006) and difficulties supporting and retaining highly qualified teachers. To address the issue of teacher retention, Lawrence has initiated a district-run program for all new teachers, pairing each of 60 new teachers with a mentor in their school who is teaching the same grade or subject area. Mentors meet regularly with the new teacher, observe him or her in class, and offer support and guidance. The intensive mentoring program costs the district $200,000 per year, a bargain compared to the $50,000 it can cost to recruit, hire, and train one new teacher. Six years ago, before the program was initiated, half of Lawrence's teachers left after their first year. Now, on average, 85% stay and 62% are still in the classroom after three years, which is 12% above the national average (Brady-Myerov 2007). The success of the local support program was recently discussed in a National Public Radio interview (Brady-Myerov 2007). In Lawrence, mentoring works. Many new teachers who leave schools after the first year report lack of support and poor working conditions as the primary reasons for leaving. New teachers can feel alone and vulnerable even working in a school building alongside scores of other teachers. At a time when many of our teachers are approaching retirement, and science and mathematics teachers are scarce, especially in urban settings, it is imperative that we support and retain teachers new to the profession. Thirty-three states now require school districts to have a teacher induction and mentoring program, but these programs vary in quality. …

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.137
Threshold uncertainty score0.460

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.002
Scholarly communication0.0100.009
Open science0.0040.015
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.1370.040

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 head0.156
GPT teacher head0.464
Teacher spread0.308 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2007
Admission routes1
Has abstractyes

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