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Record W4212916805 · doi:10.1353/ect.2008.0090

Evolution Mobile

2008· article· en· W4212916805 on OpenAlexaboutno aff
Jan DeBlieu

Bibliographic record

VenueEcotone · 2008
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSteppeGeographyAncient historyCivilizationChinaSamaraGlobeArchaeologyHistoryEthnologyEcology

Abstract

fetched live from OpenAlex

Five thousand years ago, on the rolling grassy sea of Saskatchewan, the Blackfoot people and other northern Plains tribes developed a culture based exquisitely on movement. Traveling much of the year, following bison and other game, they kept only what they could carry. Their clothing and headwear, festooned with beads and feathers, were also lightweight and sturdy. Like the Indians of spaghetti westerns, they lived in tepees, but they had no horses, only dogs. At the height of their civilization, they numbered almost two hundred thousand. So what? you may be thinking. Perhaps you’re not in the mood for an anthropology lesson. But here’s something new: On the far side of the globe, in the Samara region of Russia, other tribes were living parallel lives. There are differences between the Saskatchewan prairie and the Samara steppes, but the overall feel of the land is the same: broad horizons and undulating, treeless nonfiction Evolution Mobile Jan DeBlieu 252 Ecotone: reimagining place sweeps, with grasses lush in summer, sere in winter. And as it turns out, the prehistoric tribes in the two regions were remarkably and similarly self-sufficient. They lived in family groups of twenty to fifty people and engaged in some trade. They venerated their warriors and fought frequent battles with their neighbors. They were deeply spiritual. In time the steppe tribes bumped up against the Indo-Iranian and Chinese civilizations, so they learned how to build vehicles with wheels and, under Genghis Khan, how to build empires. The Saskatchewan nomads remained isolated and traveled on foot. There’s one last similarity between the two cultures, and it is arguably the most important. They died. They died shortly after the arrival of widespread agriculture made it possible for people to lead sedentary lives. As an exhibit at the Canadian Museum of Civilization in Quebec put it, “The end of the nomadic era came around the same time for both groups when Europeans and Euro-Americans invaded their territories and brought with them the fivefold threat of disease, firearms, immigration, agriculture, and formidable administration.” It’s a familiar story, with long-term implications. The disintegration of nomadic tribes around the globe set in motion what may turn out to be, twenty or so generations from now, one of the most profound evolutionary shifts in the history of the human race.¿Como te gusta viajar? “How do you like to travel?” I’m in Cusco, Peru, the navel of the ancient Incan empire, studying Spanish in a language -immersion program. My Spanish is pretty good, but I could use some practice in conversation, and I was hoping to find it here. I wasn’t expecting the simplistic, high school–style worksheets. Blank lines await my response; the other students are scribbling answers. I’d like to write, de cualquiera manera, meaning I love to travel and will jump at any opportunity. But I dutifully write, en tren. I prefer to travel by train. It’s a little odd to be in an ancient city that was originally built by hand, focusing on such a question of modern convenience. The following day I actually board a train heading north from Cusco, deeper into the mountains. It’s a narrow-gauge, bone-rattling line, but with spectacular views of the slate green glacial melt of the Urubamba River. We clatter through the dull terracotta mountains, fourteen thousand feet high and higher with surprisingly little snow, though it’s the dead of winter. I can’t take my eyes off the scenery. As the train bends and sways I’m constantly moving from my seat to the 253 jan deblieu aisle so I can crane up toward the summits or down at the pouring water. Large boulders crowd the riverbed, and in places the water runs more white than green. I can’t imagine that anyone could raft it and come out alive. Trees that look like cottonwood and eucalyptus grow in clumps along the banks. To either side the mountains rise steeply, their slopes dull and wrinkled, the color of corned beef. I’d love to get a real sense of this landscape , which was once a lacework of Inca and Wari...

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.006

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 head0.038
GPT teacher head0.367
Teacher spread0.329 · 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 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
Published2008
Admission routes1
Has abstractyes

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