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Record W2949843890

Annual UND Hagerty Lecture Series feature two journalists at separate events in Grand Forks and Bismarck

2015· article· en· W2949843890 on OpenAlexaboutno aff
David L. Dodds

Bibliographic record

VenueUND Scholarly Commons (University of North Dakota) · 2015
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsSeries (stratigraphy)Feature (linguistics)HistoryArtificial intelligenceComputer scienceLinguisticsPhilosophyGeology
DOInot available

Abstract

fetched live from OpenAlex

Lectureship series was established through an endowment to benefit the Communication Program This year, for the first time, the University of North Dakota will present two lectures – in Grand Forks and another in Bismarck -- as part of its annual Hagerty Lecture Series. The Grand Forks lecture, on March 24, will feature David Bjerklie, a UND graduate who writes about science for children. He will focus on responding to the profound questions about science that children ask at 7 p.m. Tuesday, March 24, in the Community Room of the Grand Forks Herald, 305 Second Avenue North in downtown Grand Forks. Enter through the alley door. The Bismarck event will feature Alexander Panetta, Washington, D.C., correspondent for the Canadian Press. He'll discuss Canadian approaches to issues arising along the international border at 4:30 p.m., Tuesday, April 21, at the North Dakota Heritage Center on the capitol grounds. David Bjerklie: Bjerklie grew up in Minot, N.D., and studied biology and anthropology at UND. As a lab and field assistant, he studied spotted sandpipers on a small island in a large lake in Minnesota. He has written on a wide range of science, medicine, technology and environment topics for Time Inc., since 1984, serving as a science reporter at Time magazine, a writer at Time books and editor at Time for Kids. He is the author of children's books on butterflies, agriculture and environmental justice. In 1989-90, he spent a year as a Knight Science Journalism Fellow at the Massachusetts Institute of Technology. In 2014, he attended the 65th annual Lindau Nobel Laureate meeting in Germany and spent three weeks in Antarctica as a National Science Foundation Media fellow. Some of his recent stories for Time for Kids have been on wind sculpture, the global explosion of jellyfish and the mathematics of juggling. He has also written chapters in recent Time books on the search for life in the universe, the use of DNA in the courtroom, artificial intelligence, the nature of collaborative genius and current research in child psychology. Alexander Panetta: A Montreal native, Panetta has worked for Canada' national news agency -- the equivalent of the Associated Press in the United States -- for 16 years. He's covered federal and provincial politics for most of that time. He's also covered international news, including the war in Afghanistan and the disastrous earthquake in Haiti. Since the fall of 2013, he's been in Washington, D.C., where he reports on U.S. stories for a Canadian audience, with a special emphasis on politics and cross boundary issues. Hagerty Lecture Series: The lecture series is named for Jack Hagerty, longtime editor of the Grand Forks Herald. When Hagerty retired in 1984, The Herald established the lectureship through an endowment to the University's Communication Program. Hagerty was the husband of Marilyn Hagerty, the Herald's food writer, whose reviews have been an Internet sensation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.433
Threshold uncertainty score0.809

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.4330.273

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 head0.045
GPT teacher head0.322
Teacher spread0.276 · 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.

Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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