MétaCan
Menu
Back to cohort
Record W4244960473 · doi:10.1179/175622708x282983

Book Reviews

2008· article· en· W4244960473 on OpenAlexaboutno aff

Bibliographic record

VenueNames · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Most toponymists agree on how to distinguish among feature classes, generics, and specifi cs.A specifi c is the unique label for a single feature, usually coupled with a generic, which, within a community of speakers, describes the feature.In Mississippi River, for example, Mississippi is the specifi c and River the generic.Its feature class, a concept which comprises all known generics sharing similar characteristics, is 'stream.'This class includes river, creek, brook, run, kill, and dozens of other generics that The Domestic Names Committee of the US Board on Geographic Names (BGN) has recorded.In this system, each generic can be assigned to one and only one of sixty-fi ve feature classes.For a list, with brief defi nitions, see http:// geonames.usgs.gov/features.html.For those who need a more comprehensive list, there are specialized dictionaries, such as the Glossary of Geology, published by the American Geological Society.The fourth edition, edited by Julia A. Jackson (1997), has some 37,000 entries covering both geological and geographical terminology.A very different way of looking at geographical terms has been provided in a new book, Home Ground: Language for an American Landscape, edited by Barry Lopez and Debra Gwartney.Best known as a nature writer, both fi ction and non-fi ction, Lopez has written extensively on the landscape and fauna of the Northwest, including northern Canada and Alaska.Two of his books, Of Wolves and Men (1978) and Arctic Dreams (1986), have become modern classics.For Home Ground, he has solicited the services of a large group of writers to defi ne those American geographical terms most familiar to each of them, terms from his or her own 'home ground.'But it is as creative writers -which includes both fi ction and non-fi ction -and not as geographers or toponomists that the contributors to this book approach their assignments.Of the forty-six contributors, twelve are primarily novelists, eight are poets, and twenty-one are non-fi ction essayists.The result is a fascinating look at how imaginative writers view the landscape and how they understand the words used to describe that landscape.Home Ground, arranged alphabetically, goes from 'a'a ¯ (a lava fl ow) to zigzag rocks (chevron-patterned dams in streams set out by Native Americans to trap fi sh).In between are some 850 separate entries of a third to a half page each in this large format book.Not just a simple defi nition, each entry is a mini-essay which defi nes the term and discusses its history and connotations.Most of these essays include a literary reference.The entry archipelago, for example, quotes a short passage from Herman Melville's Encantadas, and cut-off, where a river has cut across a narrow neck, makes generous use of Mark Twain's Life on the Mississippi.Some cut-offs are natural, Twain says; others are man-made to shorten travel time.And then he claims in his jesting way that since the distance between New Orleans and Cairo, Illinois, was shortened from 1,215 to 973 miles in the years between 1706 and 1882, in another 742 years, at that rate, 'the Lower Mississippi will be only a mile and three-quarters long.'Many of the entries are accompanied by pen and ink sketches or diagrams, all by Molly O'Halloran, greatly helping to visualize some of the features as well as adding to the visual attractiveness of this handsome book.The generous outside margins frequently feature quotations that make use of the word defi ned on that page.The margin at the entry for glade, to take one short example, has a passage from Frank Waters' The Man Who Killed the Deer: 'A

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)0.005
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: Review · Consensus signal: none
Teacher disagreement score0.600
Threshold uncertainty score0.570

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0070.004
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.6000.586

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 head0.049
GPT teacher head0.317
Teacher spread0.268 · 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
GenreReview

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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueNamesSame topicGeographic Information Systems StudiesFrench-language works237,207