O Local e a pesquisa em Educação Ambiental: a cidade como local e contexto de ensino e ação
Bibliographic record
Abstract
<!--[if gte mso 9]><xml> <o:OfficeDocumentSettings> <o:TargetScreenSize>800x600</o:TargetScreenSize> </o:OfficeDocumentSettings> </xml><![endif]--> <p class="MsoBodyText" style="line-height: normal;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;;">O objetivo desta pesquisa foi investigar como as Dissertações e Teses abordam a Educação Ambiental em âmbito municipal, relacionando as cidades estudadas com os locais de produção e, com isso,<span style="mso-spacerun: yes;"> </span>analisando a endogenia e a exogenia desses trabalhos, levando em conta os níveis municipal, estadual e<span style="mso-spacerun: yes;"> </span>regional. </span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; mso-bidi-font-family: Arial;">A pesquisa foi realizada de acordo com a metodologia preconizada por Ferreira<span style="mso-spacerun: yes;"> </span>e Romanowski e Ens (2006),acrescida de uma classificação quanto ao caráter endógeno ou exógeno das teses e dissertações em nível municipal, estadual e regional. Inicialmente, foram selecionados 741 trabalhos, entre teses e dissertações, tendo como entrada a palavra Educação Ambiental no título e/ou no resumo.<span style="mso-spacerun: yes;"> </span>Em um segundo momento, mediante a leitura dos resumos e dos sumários, 53 teses e dissertações que abordavam a Educação Ambiental em âmbito municipal foram selecionados. Outro critério utilizado para a seleção dos textos foi a disponibilidade integral para leitura e cópia eletrônica (<em style="mso-bidi-font-style: normal;">download</em>). Uma análise descritiva do material foi feita e o mesmo foi classificado quanto ao caráter endógeno/exógeno em nível municipal, estadual e regional. Todas as regiões, com, exceção da Norte, possuem produção (teses mais dissertações) endopoética maior do que exopoética. Goiás é o estado com maior produção endopoética.</span></p> <!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves /> <w:TrackFormatting /> <w:HyphenationZone>21</w:HyphenationZone> <w:PunctuationKerning /> <w:ValidateAgainstSchemas /> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF /> <w:LidThemeOther>PT-BR</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables /> <w:SnapToGridInCell /> <w:WrapTextWithPunct /> <w:UseAsianBreakRules /> <w:DontGrowAutofit /> <w:SplitPgBreakAndParaMark /> <w:EnableOpenTypeKerning /> <w:DontFlipMirrorIndents /> <w:OverrideTableStyleHps /> </w:Compatibility> <w:BrowserLevel>MicrosoftInternetExplorer4</w:BrowserLevel> <m:mathPr> <m:mathFont m:val="Cambria Math" /> <m:brkBin 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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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".