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Record W747334523 · doi:10.1353/gpq.2015.0039

Badlands: A Geography of Metaphors by Ken Delgarno (review)

2015· article· en· W747334523 on OpenAlexaboutno aff
Michael Farrell

Bibliographic record

VenueGreat plains quarterly · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsArchaeologyLandformGeologyHistoryGeographyPaleontology

Abstract

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Reviewed by: Badlands: A Geography of Metaphors by Ken Delgarno Michael Farrell Badlands: A Geography of Metaphors. By Ken Delgarno. Foreword by Ross King. Markham on: Red Deer Press, 2014. ix + 194 pp. Illustrations, bibliography. $45.00 paper. It takes an unusual personality to fall in love with badlands and then to spend a lot of time poking around in them. Either one is a romantic at heart, taken by the poetic shapes of eroded landforms and the intense colors of mineral- laced clays, or one is an outlaw on the run looking for an impenetrable secluded place to hide out where nobody in their right mind would try to follow. Ken Delgarno is clearly among the first group. His coffee table book of color photographs made in eleven southern Canadian or northern US Plains locations speaks to an obsessive compulsion to carry camera equipment into fairly inhospitable landscapes and to seek out viewpoints that portray “the most surreal and magical terrain you can imagine.” Badlands are places where the bones of the earth show through. Mostly hot and dry in the good months, home to snakes, antelope, and raptors, badlands are not easy on humans. Formed initially when the last ice age ended as retreating glaciers and rushing water sculpted canyons and valleys, badlands are usually found in semiarid regions that today are subject to the erosional forces of occasional cloudbursts and nearly constant winds. Many of the bizarre formations created by all this erosion are composed of exotic volcanic soils, like bentonite, that make for sparse vegetation, treacherous roads after a rain, and stunning visuals for those willing to take the time and effort to actually get off the road and explore. Ken Delgarno offers us a dozen or more views in each of his chosen Northern Plains locations. Many of his images, taking advantage of modern digital camera technology, make use of “high dynamic range” or nighttime “light painting” techniques. The result is a complex visual portrait of each of these hidden gems. Delgarno visited his locations in a variety of seasons and weather conditions, shooting at night and during the golden hours of sunrise or sunset. Badlands tend to have rich colors that all but disappear during the middle of the day when the sun is high, making the colors become bleached and faded. So he paid his dues to bring home the many striking images that fill this volume. My only complaint is that I wish he had included detailed maps of his chosen locations and their geographic relation to each other. It is obvious that Ken Delgarno’s literary and poetic self also fell in love with these badlands. Each photograph is captioned with a poetic title that he has found in the works of Canadian and American authors. And each chapter includes several pages of text offering personal anecdotes and outlining the location’s history and geography, great stuff like the caves where Butch Cassidy and his gang hid or the location of a wall of rock art that depicts a Blackfoot battle with enemy Plains tribes. In this sense Badlands is also a travelogue or potential visitor’s guide. [End Page 317] Michael Farrell net Television/Agricultural Leadership, Education and Communication University of Nebraska–Lincoln Copyright © 2015 Center for Great Plains Studies, University of Nebraska–Lincoln

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.305
Teacher spread0.278 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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