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Record W119254895

Skissen och skissandet - landskapsarkitektens arbetsverktyg

2009· article· en· W119254895 on OpenAlexaboutno aff
Lena Bergene

Bibliographic record

VenueEpsilon Archive for Student Projects (University of Southampton) · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsSketchProcess (computing)Computer scienceFunction (biology)Order (exchange)Focus (optics)AestheticsVisual artsArtProgramming languageAlgorithm
DOInot available

Abstract

fetched live from OpenAlex

What is sketching for a landscape architect? Does it involve anything else than the sketching process, the tool with\nwhich we work and carry a project through? The answer is yes, it is a process but one that includes many functions\nlike making notes in drawings or words for remembrance, a way to convey your ideas to others and to clearify\nideas for yourself or together in a group. This thesis aim to give a broader picture of what sketches and sketching\nis through many voices, and images made by architects, a psychiatrist, a teacher, and landscape architect students.\nThe focus is on the hand made sketch and show many examples of what a sketch can look like.\nWhat is a sketch? A sketch can be many things, but the general consensus is that it is made fast and as a draft. It\ncan be made in any material, two or three dimensional. The sketching technique you use while sketching does not\ndefi ne whether it is a sketch or not but only which technique you used. The function of a sketch changes and it will\nlook differently depending on the reason why it was made.\nWhy is it important for a landscape architect to sketch? If the tool is the sketches that I make in order to make the\nsketching process run smoothly, how do I go about to improve my sketching process? Well, four older well known\narchitects tell stories about how important it is to sketch, all the time, in order to start saving images in your head\nthat you can access at any time, in any sketching project. Then the reason for sketching becomes unimportant,\nonly the fact that we do it, will make a difference. One way to help us sketch more is to think about how we\ncan improve our ways of registering the outside world. For instance the use of the digital camera. By making\na conscious choice to use the sketch book rather than taking pictures, you learn to interpret what you see into a\nsketch.\nAs the landscape architect school around the world differ, they can be scientifi c or artistically dominated. Does\nthis mean anything in the way we communicate amongst each other? 19 students from Sweden, Slovakia, Estonia,\nEngland, Czech Republic, Canada, Germany, Austria and China answered questions on how they defi ne, use,\nlearned to sketch, fi nd joy in and are inspired by sketches within the landscape architect profession.\nThe sketching process is a method of working that can be learned by any person. The landscape architect have\ntaken this method and learned to excel in it. But this process takes many years to master. That is a reason why\nconstant refl ection on the process of sketching is so important.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.927

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 studies0.0000.000
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 head0.017
GPT teacher head0.252
Teacher spread0.235 · 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 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
Published2009
Admission routes1
Has abstractyes

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