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Record W284792651

Online Interactive Mapping: Using the Panoramic Maps Collection

2007· article· en· W284792651 on OpenAlexaboutno aff
Marsha Alibrandi, Eui kyung Shin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPlan (archaeology)CurriculumMental mappingResource (disambiguation)Social studiesGeographyVisual artsSociologyComputer sciencePsychologyArchaeologyPedagogy
DOInot available

Abstract

fetched live from OpenAlex

Remember where the train station used to be? Turn left at that corner. Often, when giving directions, we assume familiarity with historical landscapes. We expect that the listener can adjust layers of mental from the past and present to imagine a specific location. By developing students' knowledge of historical landscapes, we deepen their abilities to understand our changing physical and social geography. Our students will help plan and build the landscapes of the future.They need to value and understand the land's past if they are to create socially and environmentally sustainable communities. Many state curricula require the teaching of local history in elementary and middle grades. We would like to introduce teachers to a wonderful online resource for displaying images of local communities as they may have looked more than 100 years ago. We also suggest ways to analyze changes to the landscape by comparing these historical maps with current cartographic images. By comparing images at MapQuest with those at the Panoramic Maps Collection at a Library of Congress website, students are moving to higher analytical skills beyond simple location and magnification. When students look at landscapes around them and ask, Why is that there? they must use critical and analytical thinking skills to evaluate spatial information. History, geography, science, math, and language arts are all used to understand and interpret the information presented in these maps. Panoramic Maps Collection From the late 1840s until the early 1900s, artists drew bird's-eye view landscapes of hundreds of American cities and towns. These popular provide a detailed, if somewhat idealized, image of American communities from a past era. As the nation grew and aerial photography became possible, these maps were no longer fashionable, but today they are a teaching treasure. A map of your community (or at least one from your state) may well be in the Panoramic Map Collection, 1847-1929. landscapes were drawn as if viewed from a hot air balloon, which was the only aerial transport until 1903. Most of the research for the maps, however, was done on foot and with the use of existing maps. drawings were painstakingly rendered and made into lithographs for mass production and sales. Five artists produced most of the maps in this collection. Local chambers of commerce often funded these panoramic maps, with specific businesses paying for a featured image in the map's border art. Wealthy individuals, whose mansions might be shown in a detailed inset, underwrote some of the maps. Real estate agents and chambers of commerce used the maps with prospective buyers of homes and business properties. Some maps showed not only the existing city, but areas planned for development. People would hang framed panoramic maps of their towns prominently in their homes. In the collection, there are maps from each of the 48 contiguous states, some of which have only one map (Arizona, Delaware, and Idaho) while others have hundreds (Pennsylvania has 208). There are also maps of communities in the Canadian provinces. New York and Pennsylvania were the two most populous states between the years 1870 and 1890. Pennsylvania was a central rail hub, and many travelers passing through might have wanted a souvenir of their visit--a panoramic map! activity described below involves maps of Philadelphia, Pennsylvania, from different decades in the 1800s. Teachers may also visit The Learning Page at the Library of Congress to glean other teaching suggestions for exploiting the Panoramic Maps Collection, at memory.loc.gov/ ammem/ndlpedu/collections/pmap/. Today, the fine details on these maps can be enjoyed online. Students can zoom into a map to locate streets, neighborhoods, and specific physical features. If there is a map of your city or town, it will be fun to search for familiar places to see how they appeared more than 100 years ago. …

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.086
GPT teacher head0.413
Teacher spread0.326 · 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 designQualitative
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
Published2007
Admission routes1
Has abstractyes

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