MétaCan
Menu
Back to cohort
Record W4378223607 · doi:10.1002/ev.20543

Guest Editors’ notes

2023· article· en· W4378223607 on OpenAlexaboutno aff
John M. LaVelle, Leah C. Neubauer, Ayesha S. Boyce, Thomas Archibald

Bibliographic record

VenueNew Directions for Evaluation · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsTimelineAdventureEthosDreamPsychologySociologyOperations researchLibrary scienceComputer scienceLawHistoryPolitical scienceArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

What a long, strange trip it's been (Garcia et al., 1977). Not all who wander are lost (Tolkien, 1994). Estoy contigo (Ibarz et al., 2016). This volume has been in development since November 2019, when between AEA2019 sessions Guest Editor John LaVelle posed writing a New Directions for Evaluation on evaluator education to the NDE Editors, and received a supportive nod to move forward with developing a proposal. Following that hallway discussion, he reached out to some of the many people in evaluation that he admired; colleagues that could complement his strengths, offset his limitations, provide checks and balances to his vision, and make the project better through their contributions and critiques. Leah C. Neubauer, Ayesha S. Boyce, and Tom Archibald agreed to collaborate on the project. This group, the self-titled “Dream Team” (Leah reminded us of that name's origins, the 1992 USA men's Olympic basketball team) united around the ethos of creating an intentional space for contributors that had often not been included in discussions about evaluator education, and asking them to imagine a better future for evaluator education than we had experienced ourselves. Like any major project, there have been adventures and challenges along the way. Conceptually, we were critiqued for being a volume led solely by faculty in university settings, not being radical enough in our vision, and the accelerated timelines for submitting proposals to us for review (see Neubauer et al., 2023 ). Procedurally, when we received over forty proposals for consideration, we proposed editing two NDE volumes to incorporate as many voices as possible; when told that was not an option, we had to find ways to pare down the number of chapters while still maximizing voices in the conversation. Individually and collectively, this meant us giving up including individual chapters that addressed topics we are passionate about and find value in discussing, including chapters that we wanted to write ourselves. Humanistically, we had to find ways to recognize and support the individual and group needs of over 50 individual contributors from across the world, inclusive of scheduling and timeline challenges, writing styles, individual visions for chapters, paradigmatic and disciplinary idiosyncrasies, cultural and pan-national positionalities, and a range of experiences publishing in peer-reviewed and peer-edited outlets. All of this occurred during the COVID-19 pandemic. Upon reflection, we think these sorts of challenges are probably the rule rather than the exception. As we look at our processes, products, and connections our contributors have created, we have no regrets about the decisions we made. Our main task was straightforward but not easy; we challenged our contributors to write short pieces that addressed specific issues in evaluator education above and beyond inquiry methodology and the ubiquitous Evaluation 101. We asked them to incorporate enough previously published works to give their chapters a scholarly grounding, but to focus their efforts on their specific topic and not on the history of evaluator education writ large; we took on that topic ourselves. Indeed, in the first chapter in this volume, LaVelle and colleagues summarize the extant discourse around evaluator education and provides conceptual distinctions between education and similar topics. The final chapter by Neubauer and colleagues synthesizes the ideas introduced by the contributing authors and proposes a vision for evaluator education in the future, along with a framework for understanding our contributors. Beyond the grounding and concluding chapters, this volume is broadly grouped into six semipermeable categories. The first section focuses on what should be taught in evaluator education experiences. In chapter 2, Smith and colleagues argue for the importance of interpersonal and self-management skills so as to help evaluators better understand how they show up in their work. In chapter 3, Teasdale and colleagues discuss the importance of transparency of valuing in evaluation practice, and share teaching tools to help junior and established evaluators alike be more forthright about how they render judgements. In chapter 4, Robles-Schrader and Lemos propose the importance of helping evaluators understand the power of language itself, and describe their vision for culturally and linguistically appropriate evaluator education. Finally, in chapter 5, Villalobos and colleagues make compelling arguments for incorporating research on structural racism into evaluator education so that evaluators can better understand the historic contexts in which programs were developed and implemented. This section addresses the current and potential roles that voluntary organizations for the practice of evaluation (VOPEs) may play in helping prepare evaluators for high-quality, ethical practice. In chapter 6, Morra and Rist describe their journey developing and implementing the first IPDET training program with a focus on lessons learned and factors that contributed to their success and longevity. Next, in chapter 7, Bitar and colleagues position EvalYouth as an international movement promoting youth transformative evaluation as a mechanism for social and global change. Last, in chapter 8, Robinson and colleagues describe their experience developing and implementing AEA Local Affiliate ¡Milwaukee Evaluation! Inc. as a response to and bulwark against White Supremacy and a neoliberal agenda. This section discusses the role that community and intra-organizational education can play in helping the evaluation field flourish. In Chapter 9, Dighe describes the ways in which the Global North perspectives on evaluation can be detrimental to individuals and communities in the Global South, and suggests systematic ways of decolonizing evaluator education. In Chapter 10, Lam and colleagues challenge evaluator educators to think more critically about how teaching and learning about evaluation in organizations which may indeed yield surprising benefits and lessons learned. This section contains chapters that discuss innovative approaches to evaluator education that are housed in formal university systems. In chapter 11 Worthington and colleagues describe how their experience in an introductory evaluation course was intentionally linked with the Canadian Evaluation Society's Student Case Competition. In Chapter 12, Sperling and Márquez-Muñoz describe the unique undergraduate certificate program in evaluation at St. Mary's University in Texas. This section describes the importance of experiential learning in helping learners apply their evaluative learning. In Chapter 13, Bowman and colleagues describe a novel approach to experiential learning that brings together students, faculty, community members, and other people from across southeastern Wisconsin and helps them learn collaboratively. This section builds on the previous sections to describe the specific models educators might use to intentionally help learners engage with the evaluation field itself. In Chapter 14, Reid and colleagues describe a multi-university mentorship model in which they help their students learn about evaluation with a focus on evaluating science, technology, engineering, and math (STEM) programs. In chapter 15, Dodge Francis and colleagues describe the importance of kinship in Indigenous communities and position kinship as a possible venue for engaging Indigenous individuals so as to help them become evaluators themselves. The concluding chapter is a synthesis of the ideas offered by the contributors to this volume and challenge to the evaluation community to think deeply about evaluator education and to challenge the assumptions that we all make about the nature, topics, modalities, values, and outcomes of educational experiences.

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)0.010
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.004

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 head0.373
GPT teacher head0.569
Teacher spread0.196 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueNew Directions for EvaluationSame topicEvaluation and Performance AssessmentFrench-language works237,207