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Record W2965662900

When Evidence Doesn’t Work (Editorial)

2007· article· en· W2965662900 on OpenAlexaboutno aff
Lindsay Glynn

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Computer scienceData scienceLibrary scienceWorld Wide WebEngineering
DOInot available

Abstract

fetched live from OpenAlex

I was listening intently to a discussion on the radio recently between Newfoundland and Labrador’s Minister of Education and aprofessor from Memorial University’s Math Department. They were debating the efficacy of the math curriculum in the province’s school system. As a parent of a grade 3 student, I have my own thoughts on how the curriculum is affecting kids’ math skills (and their anxiety levels, but let’s not go there). The professor echoed the concern that parents, teachers and students have been expressing: quite simply, it’s not working. Far too many children are failing math and are struggling with the both the content and pace of the required modules. Why am I telling you this? One particular comment made by the Minister of Education struck me. She said that there was evidence to suggest that this curriculum should work. While I’m always delighted to see the evidence based practice model being used, particularly for the betterment of my kids’education, it is dismaying to see that it is not always applied well. In this particular case, evidence was collected from somewhere and a decision was made to implement a new math curriculum based on the gathered evidence. Assuming that this truly was good evidence upon which to base such a decision, then I would have to concede that the appropriate steps were taken up until that point. Unfortunately, it appears that the entire process stopped there. As we know, one of the most important components of a thorough ebp‐based implementation is an internal evaluation. What might work somewhere else is not guaranteed to work in another environment, and it is essential to determine why an implementation or intervention worked or didn’t work. It would seem, in this case, that formal evaluations of the effectiveness of the new math curriculum have not been performed and therefore, the powers that be rely solely on the fact that it worked somewhere else. This is not evidence based practice at its finest.So, what happens when evidence doesn’t work? We try to figure out why it didn’t work. Did we miss something in the critical appraisals? What is inherently different in the population or system at hand? Are there other confounders in your environment thatyou had not considered (i.e. time of year, available resources, courses being offered, etc.)? As pointed out in this issue’s commentary, a good idea is to plan your project with research and assessment in mind. Not only will you be able to track the various stages of implementation and reactions to it, it will save you the time that you may have taken weeks or months later to retrospectively evaluate. And, never to let an opportunity be wasted, I would welcome an article submission outlining an evidence based implementation that didn’t work. If it doesn’t work, it doesn’t mean that you have failed. It means that there was something you had not anticipated that had a negative effect on your intervention. We can all benefit from such information. Speaking of benefiting, I will take this opportunity to bid a fond farewell to two of EBLIP’s original Editorial Board members: Denise Koufogiannakis and Pam Ryan. Although Denise and Pam have made numerous contributions to evidence based librarianship, their work on this journal has arguably made the biggest footprint. Denise co‐founded this journal and has worked tirelessly to create an avenue for high quality publishing in this subject area. She has passionately maintained the Evidence Summaries for each issue – a task that has required a great deal of both time and expertise. She created an excellent team of writers with whom she works closely and she consistently provides feedback to ensure that first‐rate summaries are published in every issue. Pam courageously agreed to take on the task of Production Editor with the first issue. No one on the Editorial Board had experience with the journal publishing software, OJS, and Pam was able to calmly work out the bugs, respond to our calls of frustration and panic, and she is solely responsible for the final look of the publication every 3 months. She has kept Denise, Alison and me on schedule and has caught more last minute typos and formatting issues than I could possibly count. Both Denise and Pam have led the journal to where it is today as a result oftheir commitment, expertise, enthusiasm, and sincere belief in what the board is trying to accomplish through this journal. On behalf of the Editorial Board, the Evidence Summary writers, the peer reviewers, the copyeditors, and the readers, I thank them both and wish them the best success in their future endeavors. Thankfully, their future endeavors include continued involvement with this journal. Denise will continue her work with Classics Evidence Summaries, which will be a semi‐regular feature, and both Denise and Pam have joined the Editorial Advisory team. On that note, I would like to welcome two new editorial board members. Lorie Kloda has joined the board as the Associate Editor, Evidence Summaries. Lorie has been contributing to the journal as an EvidenceSummaries author. She hails from McGill University where she is currently pursuing her PhD. Katrine Mallan is assuming the role of Production Editor. Katrine currently works at the University of Calgary as an instruction librarian. Please join me in welcoming both Lorie and Katrine. They may have big shoes to fill, but they have the skills, enthusiasm and expertise to do so seamlessly.This is our last issue for the year, and it’s a big one. Aside from 7 Evidence Summaries, we are featuring 5 summaries of classic articles. Many of you will, no doubt, be familiar with these seminal papers and will be interested to read how they fare today and the impact that they have had on our profession. Also in this issue are 3 original research articles and one article outlining how to create effective questions for surveys. There is much information here to discuss on your coffee break. Well, that’s year two under our belts. Have a lovely holiday season and a very happy and safe new year. We’ll see you in 2008!

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0040.006
Open science0.0070.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0300.000

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 head0.565
GPT teacher head0.665
Teacher spread0.101 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2007
Admission routes1
Has abstractyes

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